Multiple objects tracking in surveillance video using color and Hu moments
This work addresses tracking for surveillance applications, but it appears incremental as it builds on existing feature-based methods.
The paper tackled multiple object tracking in surveillance videos by proposing a feature-based method using color and Hu moments with Chi-Square dissimilarity and nearest neighbor classifier, achieving results assessed with precision and recall metrics on benchmark datasets.
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.