Multiple complex object tracking using a combined technique

Abstract

We present a multiple object tracking framework that employs two common methods for tracking and image matching, namely Multiple Hypothesis Tracking (MHT) and Hausdorff image matching. We use the MHT algorithm to track image edges simultaneously. This algorithm is capable of tracking multiple edges with limited occlusions and is suitable for resolving any data association uncertainty caused by background clutter and closely-spaced edges. We use the Hausdorff matching algorithm to organize individual edges into objects given their two-dimensional models. The combined technique provides a robust probabilistic tracking framework which is capable of tracking complex objects in cluttered background in video sequences.