Abstract
Visual object tracking plays an essential role in vision based applications. Most of the previous Multi-pedestrian Tracking has limitations due to considering each pedestrian with the same motion and appearance model in a uniform observation space, leading to tracking failures in complex occlusions. To address this problem without losing real-time performance, we propose a graph based approach for multi-pedestrian tracking using fused vision and depth data in this paper, where one main contribution is devoted in terms of the consideration of pedestrians with different priori probability in distinguishing observation space divided based on vision and depth information. Then we formulate the tracking model using an Improved Bipartite Graph (IBG), which is then optimized with a heuristic algorithm. Experiments on three datasets of fused vision and depth data demonstrate robust tracking results of the proposed approach.
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Gao, S., Han, Z., Li, C., Jiao, J. (2013). Real-Time Multi-pedestrian Tracking Based on Vision and Depth Information Fusion. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_66
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DOI: https://doi.org/10.1007/978-3-319-03731-8_66
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