Multiple time spatial images for video-based automatic tracking of vehicles

Abstract

Video-based vehicle tracking has become an active research area due to its numerous transportation related applications. Some common challenges in traditional video-based tracking methods include initialization of tracking, tracking an unknown number of targets, sensitivity to drift from true position due to the variations in lighting condition, scene conditions and camera position in long sequences, and absence of corrective mechanism. In this thesis, a novel approach for unsupervised vehicle tracking algorithms is developed by introducing multiple time-spatial images (MTSIs)-based detection in the Monte-Carlo Particle filter or Kalman filter based-tracking. Such a use of MTSIs in tracking algorithm provides the opportunity of reliable identification of a vehicular object automatically whenever it appears in a scene. Notably, the proposed tracking method employs the concept of multiple numbers of key vehicular frames (KVFs) for each of the vehicular-objects in the traffic. These KVFs allow an accurate estimate of the centroid position of a vehicle in the key frames, due to the fact that the relative sizes of the vehicles captured in the video are maintained in these KVFs. The spatial correspondence of a vehicle in KVFs is then integrated in Particle filter or Kalman filter-based tracking as a corrective measure to alleviate the common problem of drifting and thereby increasing the accuracy in tracking trajectory. Extensive experimentations are carried out in vehicular traffics of varying environments to evaluate the tracking performance of the proposed method as compared with the existing methods. Experimental results demonstrate that the proposed approach not only automates the initialization of tracking procedure, but also increases the accuracy of tracking trajectory evaluated by the closeness of centroids of a vehicular object both in the forward and backward tracking.