Navid Mahdian

Topic

Ego-motion Aware Multi-object Tracking: An application for a ROS-based Framework

Department of Electrical and Computer Engineering

Date & location

• Friday, April 19, 2024

• 12:00 P.M.

• Virtual Defence

Reviewers

Supervisory Committee

• Dr. Homayoun Najjaran, Department of Electrical and Computer Engineering, University of Victoria (Supervisor)

• Dr. David Capson, Department of Electrical and Computer Engineering, UVic (Member) 

External Examiner

• Dr. Afzal Suleman, Department of Mechanical Engineering, University of Victoria 

Chair of Oral Examination

• Dr. Jianping Pan, Department of Computer Science, UVic

   

Abstract

Multi-object tracking (MOT) is a critical step for safe and reliable operations of robotics and autonomous systems in dynamic and cluttered environments which are inherent to real-world applications. This thesis introduces a novel MOT framework designed for the Robot Operating System (ROS), serving as a versatile foundation for the implementation, testing, and evaluation of various MOT algorithms within the realm of robotics and autonomous systems. A key hallmark of this framework is its integration with both simulated environments and real-world robotic platforms, facilitating exhaustive testing and refinement of MOT algorithms under a broad spectrum of conditions. Moreover, this comprehensive framework is distinguished by its capability for automatic ground truth generation enables detailed and systematic evaluation across numerous operational scenarios.

Within this framework, the Ego-motion Aware Target Prediction module (EMAP) is developed, which significantly enhances the performance of detection-based multi object tracking algorithms. By integrating camera motion and depth information, EMAP effectively decouples camera movement from object trajectories, thereby minimizing tracking disturbances caused by the ego-motion of the observer. EMAP’s effectiveness is rigorously demonstrated through evaluations using the KITTI dataset and a custom-generated dataset in the CARLA autonomous driving simulator, showing substantial improvements in tracking performance, especially in scenarios marked by significant camera motion or the absence of detections.

Additionally, this thesis presents a self-supervised multi-object tracking algorithm that incorporates an adaptive track-matching mechanism. This mechanism leverages unlabeled data to refine tracking precision and efficiency, reducing the dependency on extensive manual annotations and thereby enabling more scalable and generalizable tracking applications. Together, these contributions significantly advance the field of autonomous systems and robotics by paving the way for more robust and reliable multi-object tracking technologies.