Cluster-Based SJPDAFs for Classification and Tracking of Multiple Moving Objects

This paper describes a method for classifying and tracking multiple moving objects with a laser range finder (LRF). As moving objects are tracked in the framework of sample-based joint probabilistic data association filters (SJPDAFs), the proposed method is robust against occlusions or false segmentation of LRF scans. It divides tracking targets and corresponding LRF segments into clusters and able to classify each cluster as a car or a group of pedestrians. In addition, it can correct false segmentation of LRF scans. We implemented the proposed method and obtained experimental results demonstrating its effectiveness in outdoor environments and crowded indoor environments.