A review of object tracking methods: From general field to autonomous vehicles

Abstract

Object tracking is a key technology in the field of intelligent vehicle environmental perception. Accurate and efficient object tracking technologies provide autonomous vehicles with real-time information of moving objects, helping judge the behavioral intention of the objects and predict the trajectories. With the rapid development of artificial intelligence and vehicular sensors, object tracking algorithms have made great progress. Recently however, only a few studies focus on object tracking technology for autonomous vehicles, especially involving multi-source sensors and information fusion algorithms. This study systematically and comprehensively reviews over 230 studies, therein noting the recent research achievements in object tracking are generalized. First, the single object tracking (SOT) and multiple object tracking (MOT) algorithms based on vision sensor are introduced respectively, including 2D, 3D, and intelligent vehicle object tracking algorithms, while the pros and cons of the classical algorithms and latest algorithms are summarized. Then, the object tracking methods based on point cloud and multimodal fusion are discussed and analyzed, and a variety of object tracking datasets are displayed. Finally, on the basis of the research status of intelligent vehicle object tracking algorithms, the challenges and opportunities of future research are presented.