Algorithms for Multiple Ground Target Tracking

Abstract

In this thesis, multiple ground target tracking algorithms are studied. From different aspects of the ground target tracking, three different types of tracking algorithms are proposed according to the specialties of the ground target motion and sensors employed. Firstly, the dependent target tracking for ground targets is studied. State dependency is a common assumption in traditional target tracking algorithms, while this may not be the true in ground target tracking as the motion of targets are constraint to certain path. To enhance the tracking algorithm for ground targets, starting with the dependency assumption, Markov Random Field (MRF) based Probabilistic Data Association (PDA) approach is derived to associate motion dependent targets. The driving behavior model is introduced to describe motion relationship among targets. The Posterior Cramer-Rao Lower Bound (PCRLB) is derived for this new motion model. Experiments and simulations show that the proposed algorithm can reduce the false associations and improve the predictions. Eventually, the proposed approach alleviates issues like the track impurity and coalescence problem and achieves better performance comparing to standard trackers assuming state independence.

Ground target tracking using cameras is then studied. To build an efficient multi- target visual tracking algorithm, fast single target visual tracking is an important component. A novel visual tracking algorithm that has high speed and better or comparable performance to state-of-the-art trackers is proposed. The proposed approach solves the tracking task by using a mixed-motion proposal based particle filter with Ridge Regression observation likelihood calculation. This approach largely reduces the exhaustive searching in common state-of-art trackers while maintains efficient representation of the target appearance change. Experiments on 100 public benchmark videos, as well as a high frame rate benchmark, are carried out to compare the performance with the state-of-art published algorithms. The results of the experiment show the proposed tracker achieves good performance while beats other algorithms in speed with a large margin.

The proposed visual target tracker is integrated into a new multiple ground tar- get tracking algorithm using a single camera. The multi-target tracker addresses the issues in the target detection, data association and track management aside from the single target tracker. A perspective aware detection algorithm utilizing the re- cent advanced Convolutional Neural Networks (CNN) based detector is proposed to detect multiple ground targets and alleviate the weakness of CNN detectors in detecting small objects. A hierarchical class tree based multi-class data association is presented to solve the multi-class association problem with potential misclassified detections. Track management is also improved utilizing the high efficiency detectors and a Support Vector Machine (SVM) based track deletion is proposed to correctly remove the dead tracks. Benchmarking is presented in experiments and results are analyzed. A case study of applying the proposed algorithm is provided demonstrating the usefulness in real applications.