Abstract
This paper proposes detection of convoys in a crowded surveillance video. A convoy is defined as a group of pedestrians who are moving or standing together for a certain period of time. To detect such convoys, we firstly address pedestrian detection in a crowded scene, where small regions of pedestrians and their strong occlusions render usual object detection methods ineffective. Thus, we develop a method that detects pedestrian regions by clustering feature points based on their spatial characteristics. Then, positional transitions of pedestrian regions are analysed by our convoy detection method that consists of the clustering and intersection processes. The former finds groups of pedestrians in one frame by flexibly handling their relative spatial positions, and the latter refines groups into convoys by considering their temporal consistences over multiple frames. The experimental results on a challenging dataset shows the effectiveness of our convoy detection method.
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Acknowledgments
The research work by Zeyd Boukhers leading to this article has been funded by the German Academic Exchange Service (DAAD). Research and development activities in this article have been in part supported by the German Federal Ministry of Education and Research within the project “Cognitive Village: Adaptively Learning Technical Support System for Elderly” (Grant Number: 16SV7223K).
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Boukhers, Z., Wang, Y., Shirahama, K., Uehara, K., Grzegorzek, M. (2016). Convoy Detection in Crowded Surveillance Videos. In: Chetouani, M., Cohn, J., Salah, A. (eds) Human Behavior Understanding. HBU 2016. Lecture Notes in Computer Science(), vol 9997. Springer, Cham. https://doi.org/10.1007/978-3-319-46843-3_9
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DOI: https://doi.org/10.1007/978-3-319-46843-3_9
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