RISTrack: Learning Response Interference Suppression Correlation Filters for UAV Tracking

With the high computation efficiency and tracking accuracy, discriminative correlation filters (DCFs) have been applied to unmanned aerial vehicle (UAV) tracking. However, in the scenarios (i.e., complex background and temporary occlusion), DCF-based trackers usually generate low credibility response under the influence of background distractors, which contains multiple side peaks and declines the tracking performance. Motivated by the response consistency in adjacent frames and background information penalization, we propose learning a response interference suppression (RIS) CF to tackle this problem. Specifically, we introduce an RIS regularization into the DCF-based framework, which aims to keep the target area response consistent in adjacent frames and repress distractors' response in the background. Besides, we adopt a response auxiliary strategy (RAS) to smooth the target response, which intends to obtain the precise location and avoid target drift. Furthermore, extensive experiments on three UAV benchmarks demonstrate the excellent performance of the proposed method against other 19 state-of-the-art trackers. Moreover, the tracking speed of the proposed method can reach ∼ 42 frames/s (FPS) on a single CPU.