Comparative study of motion detection methods for video surveillance systems

TL;DR

This study compares twelve change detection methods for video surveillance across diverse indoor and outdoor environments using the CDnet dataset, highlighting that no single method excels in all scenarios.

Contribution

It provides a comprehensive evaluation of various change detection methods on a large-scale dataset, filling a gap in existing comparative studies.

Findings

Different methods perform best in different challenging conditions.

No single method is universally optimal across all scenarios.

The evaluation helps users select suitable methods for specific environments.

Abstract

The objective of this study is to compare several change detection methods for a mono static camera and identify the best method for different complex environments and backgrounds in indoor and outdoor scenes. To this end, we used the CDnet video dataset as a benchmark that consists of many challenging problems, ranging from basic simple scenes to complex scenes affected by bad weather and dynamic backgrounds. Twelve change detection methods, ranging from simple temporal differencing to more sophisticated methods, were tested and several performance metrics were used to precisely evaluate the results. Because most of the considered methods have not previously been evaluated on this recent large scale dataset, this work compares these methods to fill a lack in the literature, and thus this evaluation joins as complementary compared with the previous comparative evaluations. Our experimental results show that there is no perfect method for all challenging cases, each method performs well in certain cases and fails in others. However, this study enables the user to identify the most suitable method for his or her needs.