Anomaly Detection in Video Sequence with Appearance-Motion Correspondence

Autor:	Nguyen, Trong Nguyen, Meunier, Jean
Rok vydání:	2019
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	Anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose a deep convolutional neural network (CNN) that addresses this problem by learning a correspondence between common object appearances (e.g. pedestrian, background, tree, etc.) and their associated motions. Our model is designed as a combination of a reconstruction network and an image translation model that share the same encoder. The former sub-network determines the most significant structures that appear in video frames and the latter one attempts to associate motion templates to such structures. The training stage is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. The experiments on 6 benchmark datasets demonstrate the competitive performance of the proposed approach with respect to state-of-the-art methods. Comment: Paper accepted for ICCV 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1908.06351 Zobrazit plný text záznamu View this record from Arxiv