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Detection of moving objects with a moving camera using non-panoramic background model

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Abstract

This paper presents a fast and reliable method for moving object detection with moving cameras (including pan–tilt–zoom and hand-held cameras). Instead of building large panoramic background model as conventional approaches, we construct a small-size background model, whose size is the same as input frame, to decrease computation time and memory storage without loss of detection performance. The small-size background model is built by the proposed single spatio-temporal distributed Gaussian model and this can solve false detection results arising from registration error and background adaptation problem in moving background. More than the proposed background model based on spatial and temporal information, several pre- and post-processing methods are adopted and organized systematically to enhance the detection performances. We evaluate the proposed method with several video sequences under difficult conditions, such as illumination change, large zoom variation, and fast camera movement, and present outperforming detection results of our algorithm with fast computation time.

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Acknowledgments

This research is sponsored by Samsung Techwin Co., Ltd. and SNU Brain Korea 21 Information Technology program.

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Authors and Affiliations

  1. Perception and Intelligence Laboratory, ASRI, Seoul National University, Daehak-dong, Kwanak-ku, Seoul, Korea

    Soo Wan Kim, Kimin Yun, Kwang Moo Yi, Sun Jung Kim & Jin Young Choi

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  1. Soo Wan Kim
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Correspondence to Soo Wan Kim.

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Kim, S.W., Yun, K., Yi, K.M. et al. Detection of moving objects with a moving camera using non-panoramic background model. Machine Vision and Applications 24, 1015–1028 (2013). https://doi.org/10.1007/s00138-012-0448-y

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