Beyond Accuracy: Tracking more like Human via Visual Search

Abstract

Human visual search ability enables efficient and accurate tracking of an arbitrary moving target, which is a significant research interest in cognitive neuroscience. The recently proposed Central-Peripheral Dichotomy (CPD) theory sheds light on how humans effectively process visual information and track moving targets in complex environments. However, existing visual object tracking algorithms still fall short of matching human performance in maintaining tracking over time, particularly in complex scenarios requiring robust visual search skills. These scenarios often involve S patio-T emporal D iscontinuities ( i . e ., STDChallenge ), prevalent in long-term tracking and global instance tracking. To address this issue, we conduct research from a human-like modeling perspective: (1) Inspired by the CPD, we pro-pose a new tracker named CPDTrack to achieve human-like visual search ability. The central vision of CPDTrack leverages the spatio-temporal continuity of videos to introduce priors and enhance localization precision, while the peripheral vision improves global awareness and detects object movements. (2) To further evaluate and analyze STDChallenge , we create the STDChallenge Benchmark . Besides, by incorporating human subjects, we establish a human baseline, creating a high-quality environment specifically designed to assess trackers’ visual search abilities in videos across STDChallenge . (3) Our extensive experiments demonstrate