Object Tracking
Object tracking, the task of identifying and following objects in image or video sequences, aims to accurately estimate object locations and trajectories over time. Current research emphasizes improving robustness in challenging conditions (low light, underwater, camouflage) and expanding capabilities to handle multiple objects, open-vocabulary categories, and non-linear motion, often leveraging transformer networks, generative models, and multimodal fusion (e.g., combining camera and radar data). These advancements have significant implications for various fields, including autonomous driving, robotics, surveillance, and scientific studies of animal behavior, by enabling more reliable and efficient automated systems.
Papers
Improving Siamese Based Trackers with Light or No Training through Multiple Templates and Temporal Network
Ali Sekhavati, Won-Sook Lee
Hard to Track Objects with Irregular Motions and Similar Appearances? Make It Easier by Buffering the Matching Space
Fan Yang, Shigeyuki Odashima, Shoichi Masui, Shan Jiang
Automated Driving Systems Data Acquisition and Processing Platform
Xin Xia, Zonglin Meng, Xu Han, Hanzhao Li, Takahiro Tsukiji, Runsheng Xu, Zhaoliang Zhang, Jiaqi Ma