Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos
Abstract
Time-stamp aware anomaly detection in traffic videos is an essential task for the advancement of the intelligent transportation system. Anomaly detection in videos is a challenging problem due to sparse occurrence of anomalous events, inconsistent behavior of a different type of anomalies and imbalanced available data for normal and abnormal scenarios. In this paper, we present a three-stage pipeline to learn the motion patterns in videos to detect a visual anomaly. First, the background is estimated from recent history frames to identify the motionless objects. This background image is used to localize the normal/abnormal behavior within the frame. Further, we detect an object of interest in the estimated background and categorize it into anomaly based on a time-stamp aware anomaly detection algorithm. We also discuss the challenges faced in improving performance over the unseen test data for traffic anomaly detection. Experiments are conducted over Track 3 of NVIDIA AI city challenge 2019. The results show the effectiveness of the proposed method in detecting time-stamp aware anomalies in traffic/road videos.
Keywords
Cite
@article{arxiv.1906.04574,
title = {Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos},
author = {Kuldeep Marotirao Biradar and Ayushi Gupta and Murari Mandal and Santosh Kumar Vipparthi},
journal= {arXiv preprint arXiv:1906.04574},
year = {2019}
}
Comments
IEEE Computer Vision and Pattern Recognition Workshops (CVPRW-2019)