Group detection assisted by density map

Yuan Xiao; Yihua Tan

doi:10.1117/12.2541918

14 February 2020 Group detection assisted by density map

Yuan Xiao, Yihua Tan

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 114321M (2020) https://doi.org/10.1117/12.2541918
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Group detection is crucial component in intelligent video surveillance, which can capture crowd motion and directly apply to emergency security in complex scenes, thus it has attracted plenty of attention in the related fields. However, the existing works cannot fully utilize the deep and precise features of the crowd. Recently, with the rapid development of deep learning and the promotion of challenging datasets, crowd density estimation has achieved the desired accuracy in single image. Since density maps can provide a high-level semantic information for the crowd, in this paper, a density map assisted scene analysis method is proposed to detect the groups in crowd scenes. The main contributions in this study are threefold: (1) Using density map-based super-pixel segmentation method to obtain the multiple image patches, which are taken as the next research objects; (2) A group detection method based on multi-view clustering is proposed. The density maps are used to construct similar graphs from the aspects of interaction, spatial distribution, motion distribution and motion pattern. (3) A post-processing strategy is designed to combine the groups with higher relevance to determine the final group. The experimental results show that the method can accurately detect the groups in image sequence. Furthermore, compared with the existing methods, the proposed method achieves better performance on the CUHK Crowd Dataset.

Citation Download Citation

Yuan Xiao and Yihua Tan "Group detection assisted by density map", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114321M (14 February 2020); https://doi.org/10.1117/12.2541918

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KEYWORDS

Image segmentation

Image processing

Correlation function

Analytical research

Lithium

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Video surveillance

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