Robust motion tracking of multiple objects with kl-immpdaf

This paper describes how the image sequences taken by a stationary video camera may be effectively processed to detect and track moving objects against a stationary background in real-time. Our approach is first to isolate the moving objects in image sequences via a modified adaptive background estimation method and then perform token tracking of multiple objects based on features extracted from the processed image sequences. In feature based multiple object tracking, the most prominent tracking issues are track initialization, data association, occlusions due to traffic congestion, and object maneuvering. While there are limited past works addressing these problems, most relevant tracking systems proposed in the past are independently focused to either "occlusion" or "data association" only. In this paper, we propose the KL-IMMPDA (Kanade Lucas-Interacting Multiple Model Probabilistic Data Association) filtering approach for multiple-object tracking to collectively address the key issues. The proposed method essentially employs optical flow measurements for both detection and track initialization while the KL-IMMPDA filter is used to accept or reject measurements, which belong to other objects. The data association performed by the proposed KL-IMMPDA results in an effective tracking scheme, which is robust to partial occlusions and image clutter of object maneuvering. The simulation results show a significant performance improvement for tracking multiobjects in occlusion and maneuvering, when compared to other conventional trackers such as Kaiman filter.