Reducing False Alarms in Video Surveillance by Deep Feature Statistical Modeling

TL;DR

This paper introduces a deep feature statistical modeling approach combined with an a-contrario validation process to significantly reduce false alarms in video change detection, improving practical application performance.

Contribution

It presents a method-agnostic, weakly supervised validation technique based on high-dimensional statistical modeling to lower false alarms across various change detection algorithms.

Findings

Reduces false alarms at pixel and object levels.

Effective across multiple change detection methods.

Validated on diverse datasets with consistent improvements.

Abstract

Detecting relevant changes is a fundamental problem of video surveillance. Because of the high variability of data and the difficulty of properly annotating changes, unsupervised methods dominate the field. Arguably one of the most critical issues to make them practical is to reduce their false alarm rate. In this work, we develop a method-agnostic weakly supervised a-contrario validation process, based on high dimensional statistical modeling of deep features, to reduce the number of false alarms of any change detection algorithm. We also raise the insufficiency of the conventionally used pixel-wise evaluation, as it fails to precisely capture the performance needs of most real applications. For this reason, we complement pixel-wise metrics with object-wise metrics and evaluate the impact of our approach at both pixel and object levels, on six methods and several sequences from different datasets. Experimental results reveal that the proposed a-contrario validation is able to largely reduce the number of false alarms at both pixel and object levels.