Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos

TL;DR

This paper proposes a three-stage pipeline for time-stamp aware traffic video anomaly detection, addressing challenges like data imbalance and inconsistent anomaly behaviors, with experiments demonstrating its effectiveness.

Contribution

The paper introduces a novel three-stage method that learns motion patterns and localizes anomalies in traffic videos, incorporating time-stamp awareness to improve detection accuracy.

Findings

Effective detection of time-stamp aware anomalies in traffic videos.

Demonstrated robustness on NVIDIA AI City Challenge 2019 data.

Addressed challenges of data imbalance and unseen test scenarios.

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.