Vehicle Collision Detection During Night Time
Sudeep R K1, Murali Krishnan2, Pradeep R3, Sajith Vijayaraghavan4
1Sudeep R K, Dept. of Electronics & Communication, College of Engineering Trivandrum, Thiruvananthapuram, Kerala, India.
2Murali Krishnan, Dept. of Electronics & Communication, College of Engineering Trivandrum, Thiruvananthapuram, Kerala, India.
3Dr. Pradeep R, Dept. of Electronics & Communication, College of Engineering Trivandrum, Thiruvananthapuram, Kerala, India.. Email:pradeep@cet.ac.in
4Dr. Sajith Vijayaraghavan , Dept. of Electronics & Communication, College of Engineering Trivandrum, Thiruvananthapuram, Kerala, India.
Manuscript received on 01 August 2019. | Revised Manuscript received on 07 August 2019. | Manuscript published on 30 September 2019. | PP: 8560-8567 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6501098319/19©BEIESP | DOI: 10.35940/ijrte.C6501.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: ”Vehicle collision detection during night time” is an attempt to find an improved solution for vehicle detection problem, during night time. Night time vehicle detection is a challenging area since, the contrast between back ground and foreground is very less due to non uniform lighting, reflections, multiple light sources etc. This algorithm is a blend of night time image enhancement technique and fusion technique for fusing multiple features based on their weight. Image enhancement is an important aspect of the algorithm as it helps in improving the contrast between foreground and back ground as well as improving the lighting, so that it becomes easier to distinguish vehicle and other illuminated objects such as street lights. For improved reliability of the algorithm, set of complementary features – LBP (local binary pattern), HOG (Histogram of Oriented Gradients) and a CNN based feature – are used in the algorithm and are fused together using weighted fusion technique. For classification purpose, individual SVMs are trained for each feature and another SVM is trained for final rank based vehicle detection. Accurate region of interest in computed during detection phase, and is used for vehicle detection by trained SVM classifiers. Tail light detection is used for efficient region proposals. The algorithm is improved by leveraging the region proposals and high accuracy detection of Faster RCNN [10]. The algorithm detects multiple vehicles in single image frame and at possible multiple locations and distances within the frame.
Keywords: ADAS, ADS, ROI, Night Time Vehicle Detection, CNN, Faster RCNN.
Scope of the Article: Multimedia and Real-Time Communication.