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arXiv · 1610.00307

Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection

Mahdyar Ravanbakhsh·Moin Nabi·Hossein Mousavi·Enver Sangineto·Nicu Sebe
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Abstract

Most of the crowd abnormal event detection methods rely on complex hand-crafted features to represent the crowd motion and appearance. Convolutional Neural Networks (CNN) have shown to be a powerful tool with excellent representational capacities, which can leverage the need for hand-crafted features. In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality. We specifically propose a novel measure-based method which allows measuring the local abnormality in a video by combining semantic information (inherited from existing CNN models) with low-level Optical-Flow. One of the advantage of this method is that it can be used without the fine-tuning costs. The proposed method is validated on challenging abnormality detection datasets and the results show the superiority of our method compared to the state-of-the-art methods.

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Mahdyar Ravanbakhsh, Moin Nabi, Hossein Mousavi, Enver Sangineto, Nicu Sebe. 2016-10-02. Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection. https://arxiv.org/abs/1610.00307

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