Detecting dominant motion patterns in crowds of pedestrians
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of SPIE - The International Society for Optical Engineering, 2017, 10225
- Issue Date:
- 2017-01-01
Closed Access
Filename | Description | Size | |||
---|---|---|---|---|---|
SaqibPUB2040.pdf | Published version | 671.16 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2017 SPIE. As the population of the world increases, urbanization generates crowding situations which poses challenges to public safety and security. Manual analysis of crowded situations is a tedious job and usually prone to errors. In this paper, we propose a novel technique of crowd analysis, the aim of which is to detect different dominant motion patterns in real-time videos. A motion field is generated by computing the dense optical flow. The motion field is then divided into blocks. For each block, we adopt an Intra-clustering algorithm for detecting different flows within the block. Later on, we employ Inter-clustering for clustering the flow vectors among different blocks. We evaluate the performance of our approach on different real-time videos. The experimental results show that our proposed method is capable of detecting distinct motion patterns in crowded videos. Moreover, our algorithm outperforms state-of-the-art methods.
Please use this identifier to cite or link to this item: