Detecting Suspicious Behavior: How to Deal with Visual Similarity through Neural Networks

TL;DR

This paper explores using 3D CNNs to improve detection of suspicious behavior in surveillance videos, addressing high false positives caused by visual similarity among different behaviors.

Contribution

It introduces optimized 3D CNN training approaches to distinguish suspicious behaviors from normal activities despite visual similarities.

Findings

3D CNNs improve suspicious behavior detection accuracy

Optimal filter parameters enhance computational efficiency

Training approach impacts detection performance

Abstract

Suspicious behavior is likely to threaten security, assets, life, or freedom. This behavior has no particular pattern, which complicates the tasks to detect it and define it. Even for human observers, it is complex to spot suspicious behavior in surveillance videos. Some proposals to tackle abnormal and suspicious behavior-related problems are available in the literature. However, they usually suffer from high false-positive rates due to different classes with high visual similarity. The Pre-Crime Behavior method removes information related to a crime commission to focus on suspicious behavior before the crime happens. The resulting samples from different types of crime have a high-visual similarity with normal-behavior samples. To address this problem, we implemented 3D Convolutional Neural Networks and trained them under different approaches. Also, we tested different values in the number-of-filter parameter to optimize computational resources. Finally, the comparison between the performance using different training approaches shows the best option to improve the suspicious behavior detection on surveillance videos.