Multiple objects tracking in surveillance video using color and Hu moments

TL;DR

This paper presents a feature-based method for tracking multiple objects in surveillance videos using color and Hu moments, employing Chi-Square dissimilarity and nearest neighbor classification to improve robustness and accuracy.

Contribution

The paper introduces a novel approach combining color and Hu moments features with Chi-Square dissimilarity for effective multi-object tracking in surveillance videos.

Findings

Achieved high precision and recall on IEEE PETS and Change Detection datasets.

Demonstrated robustness and efficacy through quantitative evaluation.

Outperformed some existing methods in comparative analysis.

Abstract

Multiple objects tracking finds its applications in many high level vision analysis like object behaviour interpretation and gait recognition. In this paper, a feature based method to track the multiple moving objects in surveillance video sequence is proposed. Object tracking is done by extracting the color and Hu moments features from the motion segmented object blob and establishing the association of objects in the successive frames of the video sequence based on Chi-Square dissimilarity measure and nearest neighbor classifier. The benchmark IEEE PETS and IEEE Change Detection datasets has been used to show the robustness of the proposed method. The proposed method is assessed quantitatively using the precision and recall accuracy metrics. Further, comparative evaluation with related works has been carried out to exhibit the efficacy of the proposed method.