Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection
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.
Cite
@article{arxiv.1610.00307,
title = {Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection},
author = {Mahdyar Ravanbakhsh and Moin Nabi and Hossein Mousavi and Enver Sangineto and Nicu Sebe},
journal= {arXiv preprint arXiv:1610.00307},
year = {2018}
}
Comments
To appear at WACV 2018