Joint spatiotemporal regularization and scale-aware correlation filters for visual tracking

Xu, Libin; Gao, Mingliang; Li, Xuesong; Zhai, Wenzhe; Yu, Mengting; Li, Zizhan

doi:10.1117/1.jei.30.4.043011

Cited by 3 publications

(1 citation statement)

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“…One is based on SRDCF, with the addition of spatial regularization, feature dimensionality reduction, feature interpolation and confidence map fusion [24][25][26]. Another is to adopt the BACF strategy of labelling around the target region, performing spatial feature restriction and adding constraint terms to the model [27][28][29][30]. Huang et al [29] proposed a background suppressed correlation filter (BSCF) that incorporates all global background patches to enhance the tracking performance.…”

Section: Related Workmentioning

confidence: 99%

Learning Augmented Memory Joint Aberrance Repressed Correlation Filters for Visual Tracking

Sun

et al. 2022

Symmetry

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With its outstanding performance and tracking speed, discriminative correlation filters (DCF) have gained much attention in visual object tracking, where time-consuming correlation operations can be efficiently computed utilizing the discrete Fourier transform (DFT) with symmetric properties. Nevertheless, the inherent issues of boundary effects and filter degradation, as well as occlusion and background clutter, degrade the tracking performance. In this work, we proposed an augmented memory joint aberrance repressed correlation filter (AMRCF) for visual tracking. Based on the background-aware correlation filter (BACF), we introduced adaptive spatial regularity to mitigate the boundary effect. Several historical views and the current view are exploited to train the model together as a way to reinforce the memory. Furthermore, aberrance repression regularization was introduced to suppress response anomalies due to occlusion and deformation, while adopting the dynamic updating strategy to reduce the impact of anomalies on the appearance model. Finally, extensive experimental results over four well-known tracking benchmarks indicate that the proposed AMRCF tracker achieved comparable tracking performance to most state-of-the-art (SOTA) trackers.

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Section: Related Workmentioning

confidence: 99%

Learning Augmented Memory Joint Aberrance Repressed Correlation Filters for Visual Tracking

Sun

et al. 2022

Symmetry

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Spatiotemporal regularization correlation filter with response feedback

et al. 2022

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SiamGPF: temporal correlation-based visual tracking

Cao,

Zhu,

Kong

et al. 2024

J. Electron. Imag.

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A challenge in video tracking is effectively utilizing temporal correlation to navigate the complexity of a tracking scene without imposing additional computational requirements. Different from trackers that rely on online update templates, our methodology introduces a concise and efficient Siamese tracker, which assimilates temporal information via an offline strategy, named SiamGPF. Given the potential for a target to display varied appearances over time, a dependence solely on static template information proves inadequate for addressing extreme deformations and occlusions. Accordingly, we integrate the historical state of the target as pseudopriori information into our framework, using global and local cross-correlation to fuse features accurately. Furthermore, we introduce a state fusion module to enhance the interplay of information across disparate temporal points of the target. In the inference phase, we implement a cost-effective prior frame dynamic selector, adept at eliminating low-quality target information, thereby substantially augmenting the robustness of the tracker. Our tracker exhibits competitive performance across prominent tracking benchmark datasets (OTB100, LaSOT, LaSOT Ext , UAV123, NFS, TC128, GOT-10k, and VOT2016), with the official GOT-10k server evaluation results demonstrating an impressive operational speed of 212 FPS.

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Joint spatiotemporal regularization and scale-aware correlation filters for visual tracking

Cited by 3 publications

References 0 publications

Learning Augmented Memory Joint Aberrance Repressed Correlation Filters for Visual Tracking

Learning Augmented Memory Joint Aberrance Repressed Correlation Filters for Visual Tracking

Spatiotemporal regularization correlation filter with response feedback

SiamGPF: temporal correlation-based visual tracking

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