  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.
