Robust visual tracking based on global-and-local search with confidence reliability estimation

• Published in: Neurocomputing (Amsterdam) Vol. 367; pp. 273 - 286
• Main Authors: Fang, Yang, Ko, Seunghyun, Jo, Geun-Sik
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 20.11.2019
• Subjects: DCF-based tracker; Enhanced peak-to-sidelobe ratio; Global-and-local search; Target-aware detector; Visual tracking; Enhanced peak-to-sidelobe ratio; Visual tracking; DCF-based tracker; Target-aware detector; Global-and-local search
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2019.08.005

Visual object tracking is an open and challenging problem, an online tracker must be able to keep track of the target object for a long time period even in complex scenarios, such as target drift and background occlusion. Discriminative correlation filters (DCF) have shown excellent performance in short-term target tracking problems thanks to their circular dense sampling mechanism and fast computation with a discrete Fourier transform. However, they tend to drift from the target when the target encounters drastic deformation, fast motion, or background occlusion. This can result in a bad model update since the tracker searches the target in a local region centered at the position where target was located in the previous frame. There is no recovery mechanism for target re-identification and re-location. To handle this issue, this paper proposes a global-and-local-search technique that applies a DCF-based tracking model with a novel target-aware detector in a collaborative way. Our tracking model performs the local search process with high tracking confidence, and the target-aware detector is executed to re-identify and locate the target via global search from the entire frame when the model instability and confidence fluctuation are detected by proposed tracking system. Additionally, we designed an enhanced peak-to-sidelobe ratio (EPSR) for confidence estimation, which indicates system instability and fluctuation degree. Thus, the local tracking model and target-aware detector are collaboratively applied for both final target state estimation and online model updates. This not only avoids model corruption from bad updates, but also prevents our tracker from drifting problems for long-term tracking. Experiments on OTB-100 and VOT2016 benchmarks demonstrate that the proposed tracking method achieves state-of-the-art tracking performance in terms of accuracy and robustness, with 22 fps tracking speed (close to realtime) run on a single GPU.