Abstract
Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. Timely detection of traffic violations and abnormal behavior of pedestrians at public places through computer vision and visual surveillance can be highly effective for maintaining traffic order in cities. However, despite a handful of computer vision–based techniques proposed in recent times to understand the traffic violations or other types of on-road anomalies, no methodological survey is available that provides a detailed insight into the classification techniques, learning methods, datasets, and application contexts. Thus, this study aims to investigate the recent visual surveillance–related research on anomaly detection in public places, particularly on road. The study analyzes various vision-guided anomaly detection techniques using a generic framework such that the key technical components can be easily understood. Our survey includes definitions of related terminologies and concepts, judicious classifications of the vision-guided anomaly detection approaches, detailed analysis of anomaly detection methods including deep learning–based methods, descriptions of the relevant datasets with environmental conditions, and types of anomalies. The study also reveals vital gaps in the available datasets and anomaly detection capability in various contexts, and thus gives future directions to the computer vision–guided anomaly detection research. As anomaly detection is an important step in automatic road traffic surveillance, this survey can be a useful resource for interested researchers working on solving various issues of Intelligent Transportation Systems (ITS).
- B. Tian, B. T. Morris, M. Tang, Y. Liu, Y. Yao, C. Gou, D. Shen, and S. Tang. 2017. Hierarchical and networked vehicle surveillance in ITS: A survey. IEEE Transactions on Intelligent Transportation Systems 18, 1 (2017), 25--48.Google ScholarDigital Library
- A. A. Sodemann, M. P. Ross, and B. J. Borghetti. 2012. A review of anomaly detection in automated surveillance. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42, 6 (Nov. 2012), 1257--1272.Google ScholarDigital Library
- B. T. Morris and M. M. Trivedi. 2008. A survey of vision-based trajectory learning and analysis for surveillance. IEEE Transactions on Circuits and Systems for Video Technology 18, 8 (2008), 1114--1127.Google ScholarDigital Library
- S. Vishwakarma and A. Agrawal. 2013. A survey on activity recognition and behavior understanding in video surveillance. The Visual Computer 29, 10 (2013), 983--1009.Google ScholarCross Ref
- S. Sivaraman and M. M. Trivedi. 2013. Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis. IEEE Transactions on Intelligent Transportation Systems 14, 4 (2013), 1773--1795.Google ScholarDigital Library
- M. S. Shirazi and B. T. Morris. 2017. Looking at intersections: A survey of intersection monitoring, behavior and safety analysis of recent studies. IEEE Transactions on Intelligent Transportation Systems 18, 1 (2017), 4--24.Google ScholarDigital Library
- A. B. Mabrouk and E. Zagrouba. 2018. Abnormal behavior recognition for intelligent video surveillance systems: A review. Expert Systems with Applications 91 (2018), 480--491.Google ScholarDigital Library
- S. Omar, A. Ngadi, and H. H. Jebur. 2013. Machine learning techniques for anomaly detection: An overview. International Journal of Computer Applications 79, 2 (2013).Google ScholarCross Ref
- X. Li and Z.-M. Cai. 2016. Anomaly detection techniques in surveillance videos. In International Congress on Image and Signal Processing, BioMedical Engineering and Informatics. IEEE, 54--59.Google ScholarCross Ref
- K. Yun, H. Jeong, K. M. Yi, S. W. Kim, and J. Y. Choi. 2014. Motion interaction field for accident detection in traffic surveillance video. In International Conference on Pattern Recognition. IEEE, 3062--3067.Google Scholar
- D. Pathak, A. Sharang, and A. Mukerjee. 2015. Anomaly localization in topic-based analysis of surveillance videos. In Winter Conference on Applications of Computer Vision. IEEE, 389--395.Google Scholar
- W. Sultani, C. Chen, and M. Shah. 2018. Real-world anomaly detection in surveillance videos. In Conference on Computer Vision and Pattern Recognition. IEEE, 6479--6488.Google Scholar
- S. Lee, H. G. Kim, and Y. M. Ro. 2018. STAN: Spatio-temporal adversarial networks for abnormal event detection. In International Conference on Acoustics, Speech and Signal Processing. IEEE, 1323--1327.Google Scholar
- B. Tian, Q. Yao, Y. Gu, K. Wang, and Y. Li. 2011. Video processing techniques for traffic flow monitoring: A survey. In Conference on Intelligent Transportation Systems. IEEE, 1103--1108.Google Scholar
- N. Buch, S. A. Velastin, and J. Orwell. 2011. A review of computer vision techniques for the analysis of urban traffic. IEEE Transactions on Intelligent Transportation Systems 12, 3 (2011), 920--939.Google ScholarDigital Library
- X. Wang. 2013. Intelligent multi-camera video surveillance: A review. Pattern Recognition Letters 34, 1 (2013), 3--19.Google ScholarDigital Library
- N. S. Suriani, A. Hussain, and M. A. Zulkifley. 2013. Sudden event recognition: A survey. Sensors 13, 8 (2013), 9966--9998.Google ScholarCross Ref
- R. P. Loce, E. A. Bernal, W. Wu, and R. Bala. 2013. Computer vision in roadway transportation systems: A survey. Journal of Electronic Imaging 22, 4 (2013), 41--121.Google ScholarCross Ref
- P. V. K. Borges, N. Conci, and A. Cavallaro. 2013. Video-based human behavior understanding: A survey. IEEE Transactions on Circuits and Systems for Video Technology 23, 11 (2013), 1993--2008.Google ScholarDigital Library
- H. Liu, S. Chen, and N. Kubota. 2013. Intelligent video systems and analytics: A survey. IEEE Transactions on Industrial Informatics 9, 3 (2013), 1222--1233.Google ScholarCross Ref
- M. Zabłocki, K. Gościewska, D. Frejlichowski, and R. Hofman. 2014. Intelligent video surveillance systems for public spaces—a survey. Journal of Theoretical and Applied Computer Science 8, 4 (2014), 13--27.Google Scholar
- N. Patil and P. K. Biswas. 2016. A survey of video datasets for anomaly detection in automated surveillance. In International Symposium on Embedded Computing and System Design. IEEE, 43--48.Google Scholar
- S. R. E. Datondji, Y. Dupuis, P. Subirats, and P. Vasseur. 2016. A survey of vision-based traffic monitoring of road intersections. IEEE Transactions on Intelligent Transportation Systems 17, 10 (2016), 2681--2698.Google ScholarDigital Library
- Y. Li, R. Xia, Q. Huang, W. Xie, and X. Li. 2017. Survey of spatio-temporal interest point detection algorithms in video. IEEE Access 5 (2017), 10323--10331.Google ScholarCross Ref
- S. A. Ahmed, D. P. Dogra, S. Kar, and P. P. Roy. 2018. Trajectory-based surveillance analysis: A survey. IEEE Transactions on Circuits and Systems for Video Technology 29, 7 (2018), 1985--1997.Google ScholarCross Ref
- L. Lopez-Fuentes, J. van de Weijer, M. González-Hidalgo, H. Skinnemoen, and A. D. Bagdanov. 2018. Review on computer vision techniques in emergency situations. Multimedia Tools and Applications 77, 13 (July 2018), 17069--17107.Google ScholarDigital Library
- V. Chandola, A. Banerjee, and V. Kumar. 2009. Anomaly detection: A survey. ACM Computing Surveys 41, 3 (2009), 1--58.Google ScholarDigital Library
- M. Gupta, J. Gao, C. C. Aggarwal, and J. Han. 2014. Outlier detection for temporal data: A survey. IEEE Transactions on Knowledge and Data Engineering 26, 9 (2014), 2250--2267.Google ScholarCross Ref
- M. J. Roshtkhari and M. D. Levine. 2013. An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions. Computer Vision and Image Understanding 117, 10 (2013), 1436--1452.Google ScholarDigital Library
- W. Li, V. Mahadevan, and N. Vasconcelos. 2014. Anomaly detection and localization in crowded scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 1 (2014), 18--32.Google ScholarDigital Library
- H. Jeong, Y. Yoo, K. M. Yi, and J. Y. Choi. 2014. Two-stage online inference model for traffic pattern analysis and anomaly detection. Machine Vision and Applications 25, 6 (2014), 1501--1517.Google ScholarDigital Library
- X. Song, M. Wu, C. Jermaine, and S. Ranka. 2007. Conditional anomaly detection. IEEE Transactions on Knowledge and Data Engineering 19, 5 (2007), 631--645.Google ScholarDigital Library
- Y. Yuan, J. Fang, and Q. Wang. 2015. Online anomaly detection in crowd scenes via structure analysis. IEEE Transactions on Cybernetics 45, 3 (2015), 548--561.Google ScholarCross Ref
- T. Wang and H. Snoussi. 2014. Detection of abnormal visual events via global optical flow orientation histogram. IEEE Transactions on Information Forensics and Security 9, 6 (2014), 988--998.Google ScholarDigital Library
- K. Cheng, Y. Chen, and W. Fang. 2015. Gaussian process regression-based video anomaly detection and localization with hierarchical feature representation. IEEE Transactions on Image Processing 24, 12 (2015), 5288--5301.Google ScholarDigital Library
- N. Patil and P. K. Biswas. 2016. Global abnormal events detection in surveillance video—A hierarchical approach. In International Symposium on Embedded Computing and System Design. IEEE, 217--222.Google Scholar
- W. Yang, Y. Gao, and L. Cao. 2013. TRASMIL: A local anomaly detection framework based on trajectory segmentation and multi-instance learning. Computer Vision and Image Understanding 117, 10 (2013), 1273--1286.Google ScholarDigital Library
- A. C. B. Abdallah, M. Gouiffès, and L. Lacassagne. 2016. A modular system for global and local abnormal event detection and categorization in videos. Machine Vision and Applications 27, 4 (2016), 463--481.Google ScholarDigital Library
- V. Kaltsa, A. Briassouli, I. Kompatsiaris, and M. G. Strintzis. 2018. Multiple hierarchical Dirichlet processes for anomaly detection in traffic. Computer Vision and Image Understanding 169 (2018), 28--39.Google ScholarCross Ref
- X. Zhu, J. Liu, J. Wang, C. Li, and H. Lu. 2014. Sparse representation for robust abnormality detection in crowded scenes. Pattern Recognition 47, 5 (2014), 1791--1799.Google ScholarDigital Library
- P. Babaei. 2015. Vehicles behavior analysis for abnormality detection by multi-view monitoring. International Research Journal of Applied and Basic Sciences 9, 11 (2015), 1929--1936.Google Scholar
- D. Singh and C. K. Mohan. 2018. Deep spatio-temporal representation for detection of road accidents using stacked autoencoder. IEEE Transactions on Intelligent Transportation Systems 20, 3 (2018), 879--887.Google ScholarCross Ref
- C. Lu, J. Shi, and J. Jia. 2013. Abnormal event detection at 150 FPS in MATLAB. In International Conference on Computer Vision. IEEE, 2720--2727.Google Scholar
- R. Hinami, T. Mei, and Shin’ichi S.2017. Joint detection and recounting of abnormal events by learning deep generic knowledge.. In International Conference on Computer Vision. IEEE, 3619--3627.Google Scholar
- S. Zhou, W. Shen, D. Zeng, M. Fang, Y. Wei, and Z. Zhang. 2016. Spatial--temporal convolutional neural networks for anomaly detection and localization in crowded scenes. Signal Processing: Image Communication 47 (2016), 358--368.Google ScholarDigital Library
- Y. Chen, Y. Yu, and T. Li. 2016. A vision based traffic accident detection method using extreme learning machine. In International Conference on Advanced Robotics and Mechatronics. IEEE, 567--572.Google Scholar
- H. Y. T. Ngan. 2015. Outlier detection in traffic data based on the Dirichlet process mixture model. Intelligent Transport Systems 9, 7 (2015), 773--781.Google ScholarCross Ref
- D. Xu, E. Ricci, Y. Yan, J. Song, and N. Sebe. 2015. Learning deep representations of appearance and motion for anomalous event detection. Arxiv Preprint Arxiv:1510.01553.Google Scholar
- S. W. T. T. Liu, H. Y. T. Ngan, M. K. Ng, and S. J. Simske. 2018. Accumulated relative density outlier detection for large scale traffic data. Electronic Imaging 2018, 9 (2018), 1--10.Google ScholarCross Ref
- K. K. Santhosh, D. P. Dogra, and P. P. Roy. 2018. Temporal unknown incremental clustering model for analysis of traffic surveillance videos. IEEE Transactions on Intelligent Transportation Systems 20, 5 (2018), 1762--1773.Google ScholarCross Ref
- J. Wang, L. Xia, X. Hu, and Y. Xiao. 2019. Abnormal event detection with semi-supervised sparse topic model. Neural Computing and Applications 31, 5 (2019), 1607--1617.Google ScholarDigital Library
- P. Liu, P. Yang, C. Wang, K. Huang, and T. Tan. 2017. A semi-supervised method for surveillance-based visual location recognition. IEEE Transactions on Cybernetics 47, 11 (2017), 3719--3732.Google ScholarCross Ref
- S. Bhakat and G. Ramakrishnan. 2019. Anomaly detection in surveillance videos. In Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. Association for Computing Machinery, 252--255.Google Scholar
- J. R. Medel and A. Savakis. 2016. Anomaly detection in video using predictive convolutional long short-term memory networks. Arxiv Preprint Arxiv:1612.00390.Google Scholar
- L. E. Baum and T. Petrie. 1966. Statistical inference for probabilistic functions of finite state Markov chains. The Annals of Mathematical Statistics 37, 6 (1966), 1554--1563.Google ScholarCross Ref
- T. Wang, M. Qiao, Y. Deng, Y. Zhou, H. Wang, Q. Lyu, and H. Snoussi. 2018. Abnormal event detection based on analysis of movement information of video sequence. Optik-International Journal for Light and Electron Optics 152 (2018), 50--60.Google ScholarCross Ref
- M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf. 1998. Support vector machines. IEEE Intelligent Systems and their applications 13, 4 (1998), 18--28.Google Scholar
- C. E. Rasmussen. 2003. Gaussian processes in machine learning. In Summer School on Machine Learning. Springer, 63--71.Google Scholar
- M. Sabokrou, M. Fathy, M. Hoseini, and R. Klette. 2015. Real-time anomaly detection and localization in crowded scenes. In Conference on Computer Vision and Pattern Recognition Workshops. IEEE, 56--62.Google Scholar
- I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio. 2016. Deep Learning. Vol. 1. MIT Press, Cambridge.Google Scholar
- B. Babenko, M.-H. Yang, and S. Belongie. 2009. Visual tracking with online multiple instance learning. In Conference on Computer Vision and Pattern Recognition. IEEE, 983--990.Google Scholar
- S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735--1780.Google ScholarDigital Library
- W. Luo, W. Liu, and S. Gao. 2017. A revisit of sparse coding based anomaly detection in stacked RNN framework. In International Conference on Computer Vision. IEEE, 341--349.Google Scholar
- R. Girshick. 2015. Fast r-cnn. In International Conference on Computer Vision. IEEE, 1440--1448.Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3, (Jan. 2003), 993--1022.Google ScholarDigital Library
- T. Hofmann. 1999. Probabilistic latent semantic indexing. In Conference on Research and Development in Information Retrieval. ACM, 50--57.Google ScholarDigital Library
- R. Kaviani, P. Ahmadi, and I. Gholampour. 2015. Automatic accident detection using topic models. In Iranian Conference on Electrical Engineering. IEEE, 444--449.Google Scholar
- Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. 2004. Sharing clusters among related groups: Hierarchical Dirichlet processes. In Advances in Neural Information Processing Systems. MIT Press, 1385--1392.Google Scholar
- S. Bengio. 2006. Statistical machine learning from data: Gaussian mixture models. Lectures (2006).Google Scholar
- Y. Li, W. Liu, and Q. Huang. 2016. Traffic anomaly detection based on image descriptor in videos. Multimedia Tools and Applications 75, 5 (2016), 2487--2505.Google ScholarDigital Library
- J. Wen, Z. Lai, Z. Ming, W. K. Wong, and Z. Zhong. 2017. Directional Gaussian model for automatic speeding event detection. IEEE Transactions on Information Forensics and Security 12, 10 (2017), 2292--2307.Google ScholarDigital Library
- M. Ester, H.-P. Kriegel, J. Sander, X. Xu, et al. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD, Vol. 96. 226--231.Google ScholarDigital Library
- R. Ranjith, J. J. Athanesious, and V. Vaidehi. 2015. Anomaly detection using DBSCAN clustering technique for traffic video surveillance. In International Conference on Advanced Computing. IEEE, 1--6.Google Scholar
- F. Perronnin, J. Sánchez, and T. Mensink. 2010. Improving the Fisher kernel for large-scale image classification. In European Conference on Computer Vision. Springer, 143--156.Google Scholar
- I. Jolliffe. 2011. Principal component analysis. In International Encyclopedia of Statistical Science. Springer, 1094--1096.Google Scholar
- L.-L. Wang, H. Y. T. Ngan, and N. H. C. Yung. 2018. Automatic incident classification for large-scale traffic data by adaptive boosting SVM. Information Sciences 467 (2018), 59--73.Google ScholarCross Ref
- I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. MIT Press, 2672--2680.Google Scholar
- M. Ravanbakhsh, M. Nabi, E. Sangineto, L. Marcenaro, C. Regazzoni, and N. Sebe. 2017. Abnormal event detection in videos using generative adversarial nets. In International Conference on Image Processing. IEEE, 1577--1581.Google Scholar
- R. Emonet, J. Varadarajan, and J. M. Odobez. 2014. Temporal analysis of motif mixtures using Dirichlet processes. IEEE Transactions on Pattern Analysis and Machine Intelligence 36, 1 (2014), 140--156.Google ScholarDigital Library
- B. T. Morris and M. M. Trivedi. 2011. Trajectory learning for activity understanding: Unsupervised, multilevel, and long-term adaptive approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 11 (2011), 2287--2301.Google ScholarDigital Library
- K. K. Santhosh, D. P. Dogra, P. P. Roy, and B. B. Chaudhuri. 2019. Trajectory-based scene understanding using Dirichlet process mixture model. IEEE Transactions on Cybernetics (2019), 1--14.Google Scholar
- J. Varadarajan, R. Subramanian, N. Ahuja, P. Moulin, and J. Odobez. 2017. Active online anomaly detection using dirichlet process mixture model and Gaussian process classification. In Winter Conference on Applications of Computer Vision. IEEE, 615--623.Google Scholar
- V. Nguyen, D. Phung, D.-S. Pham, and S. Venkatesh. 2015. Bayesian nonparametric approaches to abnormality detection in video surveillance. Annals of Data Science 2, 1 (2015), 21--41.Google ScholarCross Ref
- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi. 2016. You only look once: Unified, real-time object detection. In Conference on Computer Vision and Pattern Recognition. IEEE, 779--788.Google Scholar
- A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei. 2014. Large-scale video classification with convolutional neural networks. In Conference on Computer Vision and Pattern Recognition. IEEE, 1725--1732.Google Scholar
- J. C. Nascimento, M. A. T. Figueiredo, and J. S. Marques. 2013. Activity recognition using a mixture of vector fields. IEEE Transactions on Image Processing 22, 5 (2013), 1712--1725.Google ScholarDigital Library
- A. Khoreva, F. Galasso, M. Hein, and B. Schiele. 2015. Classifier based graph construction for video segmentation. In Conference on Computer Vision and Pattern Recognition. IEEE, 951--960.Google Scholar
- V. Kaltsa, A. Briassouli, I. Kompatsiaris, and M. G. Strintzis. 2014. Swarm-based motion features for anomaly detection in crowds. In International Conference on Image Processing. IEEE, 2353--2357.Google Scholar
- D. P. Kingma and J. Ba. 2014. Adam: A method for stochastic optimization. Arxiv Preprint Arxiv:1412.6980.Google Scholar
- Y. Zhang, H. Lu, L. Zhang, and X. Ruan. 2016. Combining motion and appearance cues for anomaly detection. Pattern Recognition 51 (2016), 443--452.Google ScholarDigital Library
- H. Mousavi, S. Mohammadi, A. Perina, R. Chellali, and V. Mur. 2015. Analyzing tracklets for the detection of abnormal crowd behavior. In Winter Conference on Applications of Computer Vision. IEEE, 148--155.Google Scholar
- R. V. H. M. Colque, C. Caetano, M. T. L. de Andrade, and W. R. Schwartz. 2017. Histograms of optical flow orientation and magnitude and entropy to detect anomalous events in videos. IEEE Transactions on Circuits and Systems for Video Technology 27, 3 (2017), 673--682.Google ScholarDigital Library
- D. Xu, Y. Yan, E. Ricci, and N. Sebe. 2017. Detecting anomalous events in videos by learning deep representations of appearance and motion. Computer Vision and Image Understanding 156 (2017), 117--127.Google ScholarDigital Library
- V. Saligrama and Z. Chen. 2012. Video anomaly detection based on local statistical aggregates. In Conference on Computer Vision and Pattern Recognition. IEEE, 2112--2119.Google Scholar
- W. Liu, W. Luo, D. Lian, and S. Gao. 2018. Future frame prediction for anomaly detection—A new baseline. In Conference on Computer Vision and Pattern Recognition. IEEE, 6536--6545.Google Scholar
- Z. Zhang, X. Mei, and B. Xiao. 2016. Abnormal event detection via compact low-rank sparse learning. IEEE Intelligent Systems 31, 2 (2016), 29--36.Google ScholarDigital Library
- B. Yu, Y. Liu, and Q. Sun. 2017. A content-adaptively sparse reconstruction method for abnormal events detection with low-rank property. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, 4 (2017), 704--716.Google ScholarCross Ref
- M. Hasan, J. Choi, J. Neumann, A. K. Roy-Chowdhury, and L. S. Davis. 2016. Learning temporal regularity in video sequences. In Conference on Computer Vision and Pattern Recognition. IEEE, 733--742.Google Scholar
- D. Wijayasekara, O. Linda, M. Manic, and C. G. Rieger. 2014. Mining building energy management system data using fuzzy anomaly detection and linguistic descriptions. IEEE Transactions on Industrial Informatics 10, 3 (2014), 1829--1840.Google ScholarCross Ref
- Y. Li, T. Guo, R. Xia, and W. Xie. 2018. Road traffic anomaly detection based on fuzzy theory. IEEE Access 6 (2018), 40281--40288.Google ScholarCross Ref
- M.-C. Chang, Y. Wei, N. Song, and S. Lyu. 2018. Video analytics in smart transportation for the AIC ’18 challenge. In Conference on Computer Vision and Pattern Recognition Workshops. IEEE, 61--68.Google Scholar
- S. C. Lee and R. Nevatia. 2014. Hierarchical abnormal event detection by real time and semi-real time multi-tasking video surveillance system. Machine Vision and Applications 25, 1 (2014), 133--143.Google ScholarDigital Library
- Z. Chen, Y. Tian, W. Zeng, and T. Huang. 2015. Detecting abnormal behaviors in surveillance videos based on fuzzy clustering and multiple auto-encoders. In International Conference on Multimedia and Expo. IEEE, 1--6.Google Scholar
- X. Mo, V. Monga, R. Bala, and Z. Fan. 2014. Adaptive sparse representations for video anomaly detection. IEEE Transactions on Circuits and Systems for Video Technology 24, 4 (2014), 631--645.Google ScholarCross Ref
- N. B. Ghrab, E. Fendri, and M. Hammami. 2016. Abnormal events detection based on trajectory clustering. In International Conference on Computer Graphics, Imaging and Visualization. IEEE, 301--306.Google Scholar
- A. Kumar and C. J. Prabhakar. 2017. Vehicle abnormality detection and classification using model based tracking. International Journal of Advanced Research in Computer Science 8, 5 (2017).Google Scholar
- O. Isupova, D. Kuzin, and L. Mihaylova. 2016. Anomaly detection in video with Bayesian nonparametrics. In ICMLW.Google Scholar
- S. Coşar, G. Donatiello, V. Bogorny, C. Garate, L. O. Alvares, and F. Brémond. 2017. Toward abnormal trajectory and event detection in video surveillance. IEEE Transactions on Circuits and Systems for Video Technology 27, 3 (2017), 683--695.Google ScholarDigital Library
- Z. Fang, F. Fei, Y. Fang, C. Lee, N. Xiong, L. Shu, and S. Chen. 2016. Abnormal event detection in crowded scenes based on deep learning. Multimedia Tools and Applications 75, 22 (2016), 14617--14639.Google ScholarDigital Library
- M. Sabokrou, M. Fayyaz, M. Fathy, Z. Moayed, and R. Klette. 2018. Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes. Computer Vision and Image Understanding 172 (2018), 88--97.Google ScholarCross Ref
- P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie. 2005. Behavior recognition via sparse spatio-temporal features. In International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance. IEEE, 65--72.Google Scholar
- Y. Zhang, H. Lu, L. Zhang, X. Ruan, and S. Sakai. 2016. Video anomaly detection based on locality sensitive hashing filters. Pattern Recognition 59 (2016), 302--311.Google ScholarDigital Library
- J. Lan, Y. Jiang, G. Fan, D. Yu, and Q. Zhang. 2016. Real-time automatic obstacle detection method for traffic surveillance in urban traffic. Journal of Signal Processing Systems 82, 3 (2016), 357--371.Google ScholarDigital Library
- H. Wang and C. Schmid. 2013. Action recognition with improved trajectories. In International Conference on Computer Vision. IEEE, 3551--3558.Google Scholar
- W. Lin, Y. Zhou, H. Xu, J. Yan, M. Xu, J. Wu, and Z. Liu. 2017. A tube-and-droplet-based approach for representing and analyzing motion trajectories. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 8 (2017), 1489--1503.Google ScholarDigital Library
- T. N. Nguyen and J. Meunier. 2019. Anomaly detection in video sequence with appearance-motion correspondence. In International Conference on Computer Vision. IEEE, 1273--1283.Google Scholar
- M. K. Lim, S. Tang, and C. S. Chan. 2014. iSurveillance: Intelligent framework for multiple events detection in surveillance videos. Expert Systems with Applications 41, 10 (2014), 4704--4715.Google ScholarCross Ref
- F. Mehboob, M. Abbas, R. Jiang, A. Rauf, S. A. Khan, and S. Rehman. 2017. Trajectory based vehicle counting and anomalous event visualization in smart cities. Cluster Computing (2017), 1--10.Google Scholar
- S. Zhou, W. Shen, D. Zeng, and Z. Zhang. 2015. Unusual event detection in crowded scenes by trajectory analysis. In International Conference on Acoustics, Speech and Signal Processing. IEEE, 1300--1304.Google Scholar
- J. Kennedy. 2011. Particle swarm optimization. In Encyclopedia of Machine Learning. Springer, 760--766.Google Scholar
- P. Giannakeris, V. Kaltsa, K. Avgerinakis, A. Briassouli, S. Vrochidis, and I. Kompatsiaris. 2018. Speed estimation and abnormality detection from surveillance cameras. In Conference on Computer Vision and Pattern Recognition Workshops. IEEE, 93--99.Google Scholar
- H. Xia, T. Li, W. Liu, X. Zhong, and J. Yuan. 2019. Abnormal event detection method in surveillance video based on temporal CNN and sparse optical flow. In International Conference on Computing and Data Engineering. ACM, 90--94.Google Scholar
- Ö. Aköz and M. E. Karsligil. 2014. Traffic event classification at intersections based on the severity of abnormality. Machine Vision and Applications 25, 3 (2014), 613--632.Google ScholarDigital Library
- L. Patino and J. Ferryman. 2014. Multiresolution semantic activity characterisation and abnormality discovery in videos. Applied Soft Computing 25 (2014), 485--495.Google ScholarDigital Library
- S. Xia, J. Xiong, Y. Liu, and G. Li. 2015. Vision-based traffic accident detection using matrix approximation. In Asian Control Conference. IEEE, 1--5.Google Scholar
- CAVIAR. Video Dataset. Retrieved on July 15, 2020 from http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/.Google Scholar
- AVSS2007. i-LIDS. Retrieved on July 15, 2020 from http://www.eecs.qmul.ac.uk/ andrea/avss2007_d.html.Google Scholar
- NGSIM. Next Generation Simulation Community. Retrieved on July 15, 2020 from https://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm.Google Scholar
- A. Ellis, A. Shahrokni, and J. M. Ferryman. 2009. PETS2009 and Winter-PETS 2009 results: A combined evaluation. In International Workshop on Performance Evaluation of Tracking and Surveillance. IEEE, 1--8.Google Scholar
- J. Varadarajan and J.-M. Odobez. 2009. Topic models for scene analysis and abnormality detection. In International Conference on Computer Vision Workshops. IEEE, 1338--1345.Google ScholarCross Ref
- R. Mehran, A. Oyama, and M. Shah. 2009. Abnormal crowd behavior detection using social force model. In Conference on Computer Vision and Pattern Recognition. IEEE, 935--942.Google Scholar
- Y. Benezeth, P.-M. Jodoin, V. Saligrama, and C. Rosenberger. 2009. Abnormal events detection based on spatio-temporal co-occurences. In Conference on Computer Vision and Pattern Recognition. IEEE, 2458--2465.Google Scholar
- X. Wang, X. Ma, and W. E. L. Grimson. 2009. Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 3 (2009), 539--555.Google ScholarDigital Library
- V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos. 2010. Anomaly detection in crowded scenes. In Conference on Computer Vision and Pattern Recognition. IEEE, 1975--1981.Google Scholar
- A. Zaharescu and R. Wildes. 2010. Anomalous behaviour detection using spatiotemporal oriented energies, subset inclusion histogram comparison and event-driven processing. In European Conference on Computer Vision. Springer, 563--576.Google Scholar
- S. Blunsden and R. B. Fisher. 2010. The BEHAVE video dataset: Ground truthed video for multi-person behavior classification. Annals of the British Machine Vision Association 4, 1--12 (2010), 4.Google Scholar
- X. Wang, K. T. Ma, G.-W. Ng, and W. E. L. Grimson. 2011. Trajectory analysis and semantic region modeling using nonparametric hierarchical Bayesian models. International Journal of Computer Vision 95, 3 (2011), 287--312.Google ScholarDigital Library
- T. Hospedales, S. Gong, and T. Xiang. 2012. Video behaviour mining using a dynamic topic model. International Journal of Computer Vision 98, 3 (2012), 303--323.Google ScholarDigital Library
- NVIDIA. NVIDIA AI CITY. Retrieved on July 15, 2020 from https://www.aicitychallenge.org/.Google Scholar
- M. Chebiyyam, R. D. Reddy, D. P. Dogra, H. Bhaskar, and L. Mihaylova. 2017. Motion anomaly detection and trajectory analysis in visual surveillance. Multimedia Tools and Applications (2017), 1--26.Google Scholar
- K. Yun, Y. Yoo, and J. Y. Choi. 2017. Motion interaction field for detection of abnormal interactions. Machine Vision and Applications 28, 1--2 (2017), 157--171.Google ScholarDigital Library
- A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz. 2008. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 3 (2008), 555--560.Google ScholarDigital Library
- M. Ye, X. Peng, W. Gan, W. Wu, and Y. Qiao. 2019. AnoPCN: Video anomaly detection via deep predictive coding network. In International Conference on Multimedia. ACM, 1805--1813.Google Scholar
- Y. S. Chong and Y. H. Tay. 2017. Abnormal event detection in videos using spatiotemporal autoencoder. In International Symposium on Neural Networks. Springer, 189--196.Google Scholar
- D. Danilescu, A. Lodin, L. Grama, and C. Rusu. 2015. Road anomalies detection using basic morphological algorithms. Carpathian Journal of Electronic and Computer Engineering 8, 2 (2015), 15.Google Scholar
- T. R. Ionescu, S. Smeureanu, B. Alexe, and M. Popescu. 2017. Unmasking the abnormal events in video. In IEEE International Conference on Computer Vision. IEEE, 2895--2903.Google Scholar
- W. Luo, W. Liu, and S. Gao. 2017. Remembering history with convolutional LSTM for anomaly detection. In International Conference on Multimedia and Expo. IEEE, 439--444.Google Scholar
- Y. Zhao, B. Deng, C. Shen, Y. Liu, H. Lu, and X.-S. Hua. 2017. Spatio-temporal autoencoder for video anomaly detection. In International Conference on Multimedia. ACM, 1933--1941.Google ScholarDigital Library
- Q. Sun, H. Liu, and T. Harada. 2017. Online growing neural gas for anomaly detection in changing surveillance scenes. Pattern Recognition 64 (2017), 187--201.Google ScholarDigital Library
- S. Ojha and S. Sakhare. 2015. Image processing techniques for object tracking in video surveillance—A survey. In International Conference on Pervasive Computing. IEEE, 1--6.Google Scholar
- C. Chen, Q. Chen, J. Xu, and V. Koltun. 2018. Learning to see in the dark. In Conference on Computer Vision and Pattern Recognition. IEEE, 3291--3300.Google Scholar
- W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. 2016. Ssd: Single shot multibox detector. In European Conference on Computer Vision. Springer, 21--37.Google Scholar
- N. Wojke, A. Bewley, and D. Paulus. 2017. Simple online and realtime tracking with a deep association metric. In International Conference on Image Processing. IEEE, 3645--3649.Google Scholar
Index Terms
- Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey
Recommendations
A robust fusion method for vehicle detection in road traffic surveillance
ICIC'10: Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computingVehicle detection is an essential task in the intelligent transportation system, which will affects the performance of surveillance directly. This paper presents an approach to detect vehicle from a sequence of traffic images obtained from expressway ...
Anomaly Detection for Road Traffic: A Visual Analytics Framework
The analysis of large amounts of multidimensional road traffic data for anomaly detection is a complex task. Visual analytics can bridge the gap between computational and human approaches to detecting anomalous behavior in road traffic, making the data ...
Anomaly Detection in SMTP Traffic
ITNG '06: Proceedings of the Third International Conference on Information Technology: New Generationsfor detecting SMTP traffic anomaly. Our detection method cumulates the deviation of current delivering status from history behavior based on a weighted sum method called the leaky integrate-and-fire model to detect anomaly. The simplicity of our ...
Comments