Detecting dominant motion patterns in crowds of pedestrians

Muhammad Saqib; Sultan Daud Khan; Michael Blumenstein

doi:10.1117/12.2266825

8 February 2017 Detecting dominant motion patterns in crowds of pedestrians

Muhammad Saqib, Sultan Daud Khan, Michael Blumenstein

Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102251L (2017) https://doi.org/10.1117/12.2266825
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan

Abstract

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.

Citation Download Citation

Muhammad Saqib, Sultan Daud Khan, and Michael Blumenstein "Detecting dominant motion patterns in crowds of pedestrians", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102251L (8 February 2017); https://doi.org/10.1117/12.2266825

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