A Feature-based Vehicle Tracking Algorithm Using Merge and Split-based Hierarchical Grouping

Vehicle tracking is an important issue in Intelligence Transportation Systems (ITS) to estimate the location of vehicle in the next frame. In this paper، a feature-based vehicle tracking algorithm using Kanade-Lucas-Tomasi (KLT) feature tracker is developed. In this algorithm، a merge and split-based hierarchical two-stage grouping algorithm is proposed to represent vehicles from the tracked features. In the proposed grouping algorithm، with defining measures such as distance، spread and also blob analysis، initial grouping results formed by K-means clustering algorithm are refined. Moreover، to modify the performance of KLT tracker and also optimized utilization from grouping results obtained by proposed algorithm، an effective group matching algorithm based on a merging and splitting scheme is employed to match the tracked groups from a frame to the next frame. The proposed tracking algorithm is evaluated on different test videos with various illumination conditions such as day، night and shadow. The obtained results show that our proposed tracking algorithm covers the most challenges of tracking in the ITS applications.