Dynamic object tracking for self-driving cars using monocular camera and LIDAR

The detection and tracking of dynamic traffic participants (e.g., pedestrians, cars, and bicyclists) plays an important role in reliable decision-making and intelligent navigation for autonomous vehicles. However, due to the rapid movement of the target, most current vision-based tracking methods, which perform tracking in the image domain or invoke 3D information in parts of their pipeline, have real-life limitations such as lack of the ability to recover tracking after the target is lost. In this work, we overcome such limitations and propose a complete system for dynamic object tracking in 3D space that combines: (1) a 3D position tracking algorithm based on monocular camera and LIDAR for the dynamic object; (2) a re-tracking mechanism (RTM) that restore tracking when the target reappears in camera's field of view. Compared with the existing methods, each sensor in our method is capable of performing its role to preserve reliability, and further extending its functions through a novel multimodality fusion module. We perform experiments in the real-world self-driving environment and achieve a desired 10Hz update rate for real-time performance. Our quantitative and qualitative analysis shows that this system is reliable for dynamic object tracking purposes of self-driving cars.