Video Crowd Localization with Multi-focus Gaussian Neighbor Attention and a Large-Scale Benchmark

• URL: http://arxiv.org/abs/2107.08645v2
• Date: Tue, 20 Jul 2021 01:46:08 GMT
• Title: Video Crowd Localization with Multi-focus Gaussian Neighbor Attention and a Large-Scale Benchmark
• Authors: Haopeng Li, Lingbo Liu, Kunlin Yang, Shinan Liu, Junyu Gao, Bin Zhao, Rui Zhang, Jun Hou
• Abstract summary: We develop a unified neural network called GNANet to accurately locate head centers in video clips. To facilitate future researches in this field, we introduce a large-scale crowded video benchmark named SenseCrowd. The proposed method is capable to achieve state-of-the-art performance for both video crowd localization and counting.
• Score: 35.607604087583425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos. To model spatial-temporal dependencies of human mobility, we propose a multi-focus Gaussian neighbor attention (GNA), which can effectively exploit long-range correspondences while maintaining the spatial topological structure of the input videos. In particular, our GNA can also capture the scale variation of human heads well using the equipped multi-focus mechanism. Based on the multi-focus GNA, we develop a unified neural network called GNANet to accurately locate head centers in video clips by fully aggregating spatial-temporal information via a scene modeling module and a context cross-attention module. Moreover, to facilitate future researches in this field, we introduce a large-scale crowded video benchmark named SenseCrowd, which consists of 60K+ frames captured in various surveillance scenarios and 2M+ head annotations. Finally, we conduct extensive experiments on three datasets including our SenseCrowd, and the experiment results show that the proposed method is capable to achieve state-of-the-art performance for both video crowd localization and counting. The code and the dataset will be released.

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