Multiple Object Tracking Based on a Hierarchical Clustering of Features Approach

One challenge in object tracking is to develop algorithms for automated detection and tracking of multiple objects in real time video sequences. In this paper, we have proposed a new method for multiple object tracking based on the hierarchical clustering of features. First, the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest and the hierarchical clustering approach is then applied to cluster and form them into feature blocks. These feature blocks will be used to track the objects frame by frame. Experimental results show evidence that the proposed method is highly effective in detecting and tracking multiple objects in real time video sequences.