Visual Tracking via Adaptive Spatially-Regularized Correlation Filters
By Kenan Dai, Dong Wang, Huchuan Lu, Chong Sun, Jianhua Li. Computer Vision and Pattern Recognition, 2019
310 citations recorded by the source corpus.
In this work, we propose a novel adaptive spatially-regularized correlation filters (ASRCF) model to simultaneously optimize the filter coefficients and the spatial regularization weight. First, this adaptive spatial regularization scheme could learn an effective spatial weight for a specific object and its appearance variations, and therefore result in more reliable filter coefficients during the tracking process. Second, our ASRCF model can be effectively optimized based on the alternating direction method of multipliers, where each subproblem has the closed-from solution. Third, our tracker applies two kinds of CF models to estimate the location and scale respectively. The location CF model exploits ensembles of shallow and deep features to determine the optimal position accurately. The scale CF model works on multi-scale shallow features to estimate the optimal scale efficiently. Extensive experiments on five recent benchmarks show that our tracker performs favorably against many state-of-the-art algorithms, with real-time performance of 28fps.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/e5820233ea8abd27c81c0a83b1b782a9d4ca8db2
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In Computer Vision and Pattern Recognition
- Luc Van Gool
- Martin Danelljan
- Ming-Hsuan Yang
- Philip H. S. Torr
- Radu Timofte
- Dacheng Tao
- Chen Change Loy
- Xiaogang Wang
Citations in the corpus
- Transforming Model Prediction for Tracking (2022, background): “While a variety of target models are used in the literature [1, 9, 23, 29, 35, 39, 47], discriminative trackers share a common base formulation to produce the target model weights.”
- Deep Learning for Visual Tracking: A Comprehensive Survey (2019, methodology): “HCFTs [133], adaDDCF [134], YCNN [135], DeepHPFT [136], CFCF [137], CFSRL [138], P2T [139], DCDCF [140], FICFNet [141], LCTdeep [142], HSTC [143], DeepFWDCF [144], CF-FCSiam [145], MGNet [146], ORHF [147], ASRCF [148], ATOM [149], C-RPN [150], GCT [151], RPCF [152], SPM [153], SiamDW [154], SiamMask”
- LaSOT: A High-quality Large-scale Single Object Tracking Benchmark (2020, methodology): “, HCFT [62], DSTRCF [50], ECO [14], GFSDCF [85], ASRCF [12]) and regularization techniques (e.”
- Siam R-CNN: Visual Tracking by Re-Detection (2019, methodology): “Other recent developments in VOT include using domain specific layers with online learning [50], learning an adaptive spatial filter regularizer [14], exploiting categoryspecific semantic information [61], using continuous [17] or factorized [15] convolutions, and achieving accurate bounding box pre”
- Know Your Surroundings: Exploiting Scene Information for Object Tracking (2020, background): “Most current approaches [3, 8, 16, 25, 30, 33, 36] tackle the problem by learning an appearance model of the target in the initial frame.”
- AutoTrack: Towards High-Performance Visual Tracking for UAV With Automatic Spatio-Temporal Regularization (2020, methodology): “Compared to deep trackers including ASRCF [31], TADT [51], SiameseFC [15], DSiam [56], MCCT [13], ADNet [16], CFNet [52], DeepSTRCF [14], UDT+ [53], HCF [18], C-COT [17], ECO [20], IBCCF [54], MCPF [19] and CREST [35], AutoTrack with a single CPU exhibits the”