Frame-Sub Sampled, Drift-Resilient Long-Term Video Object Tracking

Abstract

A novel frame-subsampled, drift-resilient (FSDR) video object tracking (VOT) algorithm is proposed. Two design goals are sought: to improve the accuracy and to reduce the processing time. The drifting problem is mitigated with a drift detector and accompanying drift recovery mechanism. When a drift is detected, the recovery mechanism provides an opportunity to put the tracking back on track. To gather context-dependent statistics required for these procedures, an initial short segment of the video sequence will be used as a training sequence. Thus, this algorithm is more suitable for video sequences much longer than several minutes. To reduce computing time, a novel frame-subsampling strategy is proposed to process the VOT on small subset of frames. The trajectory of the tracked object on frames that are skipped will be estimated via interpolation. Compared with state of art VOT algorithms, dramatic improvement of performance (accuracy) and orders of magnitude computing time reduction are observed.