Data Association and Prediction for Tracking Multiple Objects

Abstract

Objectives: Tracking moving objects is essential for high level computer vision analysis, like object behavior interpretation or gait recognition etc. In this paper, a new method to track multiple moving objects in the surveillance video sequence is proposed. Methods/Statistical Analysis: Object tracking is done by extracting color moments feature from the segmented foreground object and associating individual objects in the successive frames using nearest neighbor classifier and Chi- Square dissimilarity measure. Further, object tracking during occlusion has been addressed by predicting the missing state of the occluded object by applying Lagrange’s polynomial extrapolation using the apriori knowledge. Findings: The proposed method is evaluated using several challenging sequences of the benchmark IEEE PETS, IEEE CHANGE DETECTION, and EPFL dataset. Further, comparative evaluation with contemporary methods has been carried out to corroborate the efficacy of the proposed method. Application/Improvements: This work focuses on devising a new method to track multiple moving objects using color moments and Lagrange’s extrapolation framework which works for a complex environment and in the presence of occlusions. Further, the shadow elimination task after the motion segmentation could be considered as an improvement step in this work which will aid in better tracking results.