Reliable Re-Detection for Long-Term Tracking

Abstract

In long-term object tracking, severe occlusion and deformation could happen to the targets. Due to the accumulation and propagation of estimation errors, even a few frames of full occlusion in a video sequence could lead to the failure of the tracking. Recently, correlation filter-based trackers have received lots of attention and gained great success in real-time tracking. However, most of them ignore the reliability of the tracked results and lack an effective mechanism to refine the unreliable results. To cope with these issues, in this paper, we propose a long-term tracking framework composed of both tracking-by-detection and re-detection modules. The tracking-by-detection part is built on the discriminative correlation filter (DCF) integrated with a color-based model. The re-detection module filters a large number of detection candidates and refines the tracking results. With the proposed re-detection refinement, detected results in each frame were re-evaluated and re-detection is carried out when necessary. Besides, the reliability estimation in the re-detection module also helps adaptively update the object detector and keep it from corruption. The proposed re-detection module can be integrated into correlation filter-based trackers to consistently boost the performance. Extensive experiments on the OTB-2015, Temple-Color, and VOT-2015 benchmarks show that the proposed method performs favorably against the state-of-the-art methods while still running faster than 40 f/s.