Discriminative Correlation Filter Tracker with Channel and Spatial Reliability

By Alan Lukezic, Tomás Vojír, L. Č. Zajc, Jiri Matas, Matej Kristan. International Journal of Computer Vision, 2016

835 citations recorded by the source corpus.

Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard feature sets, HoGs and colornames, the novel CSR-DCF method—DCF with channel and spatial reliability—achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs close to real-time on a CPU.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8cc3651488e02d51fa5ae8d3563b346e9e370f5a

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Citations in the corpus

High Performance Visual Tracking with Siamese Region Proposal Network (2018, methodology): “In this experiment, we compare our method with several representive trackers, including PTAV [11], CREST[31], SRDCF [8], SINT [33], CSR-DCF [23], Siamese-FC [4], Staple [3], CFNet [35] and DSST [9].”
Distractor-aware Siamese Networks for Visual Object Tracking (2018, methodology): “We evaluate the proposed algorithms with numerous fast and state-of-the-art trackers including SiamFC [2], CFNet [33], Staple [1], CSRDCF [19], BACF [11], ECO-HC [3], CREST [29], MDNet [23], CCOT [5], ECO [3], and the baseline tracker SiamRPN [16].”
LaSOT: A High-quality Large-scale Single Object Tracking Benchmark (2020, background): “, SRDCF [18], STRCF [50], BACF [31], Staple CA [66] and CSRDCF [61]), ensemble trackers (e.”
A Twofold Siamese Network for Real-Time Object Tracking (2018, neutral): “VOT2017 benchmark: Table 6 shows the comparison of SA-Siam with ECOhc[6], CSRDCF++[25], UCT[41], SiamFC[3], SiamDCF[35] and Staple[2] on the VOT2017 benchmark.”
Deep Learning for Visual Tracking: A Comprehensive Survey (2019, methodology): “DeepSRDCF [52] VGG-M Conv5 Still images ImageNet HOG, DAF N/A, GPU Matlab MatConvNet N/A CM CCOT [56] VGG-M Conv1, Conv5 Still images ImageNet HOG, CN, DAF N/A, GPU Matlab MatConvNet 1 CM ECO [87] VGG-M Conv1, Conv5 Still images ImageNet HOG, CN, DAF N/A, GPU Matlab MatConvNet 8 CM DeepCSRDCF [88] V”
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild (2018, methodology): “We chose among the following trackers: ECO [12], CSRDCF [44], BACF [30], SiameseFC [14], STAPLECA [31], STAPLE [26], SRDCF [45], SAMF [46], CSK [47], KCF [48], DCF [48] and MOSSE [23].”
Learning Dynamic Memory Networks for Object Tracking (2018, methodology): “To further show our tracking accuracy, we also compared with another 8 recent state-of-the art trackers that are not real-time speed, including CREST [26], CSR-DCF [19], MCPF [38], SRDCFdecon [9], SINT [29], SRDCF [6], HDT [23], HCF [20] on OTB-2015.”
Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking (2018, neutral): “058 CSRDCF [31] 0.”
Visual Tracking via Adaptive Spatially-Regularized Correlation Filters (2019, methodology): “The compared trackers include LSART [31], ECO [8], CFCF [16], GNet, MCCT [34], C-COT [13], CSRDCF [28], SiamDCF, MCPF [37] and CRT [6].”
The Sixth Visual Object Tracking VOT2018 Challenge Results (2018, methodology): “%) applied discriminative correlation filters (ANT A.3, BoVW CFT A.4, CCOT A.11, CFCF A.13, CFTR A.15, CPT A.7, CPT fast A.8, CSRDCF A.24, CSRTPP A.25, CSTEM A.9, DCFCF A.22, DCFNet A.18, DeepCSRDCF A.17, DeepSTRCF A.20, DFPReco A.29, DLSTpp A.28, DPT A.21, DRT A.16, DSST A.26, ECO A.31, HMMTxD A.53”
Deeper and Wider Siamese Networks for Real-Time Visual Tracking (2019, background): “Surprisingly, even the plain SiamFC+ surpasses the real-time tracker champion of vot2017 CSRDCF++[23] by 2.”
Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark (2021, methodology): “Tracking by Bounding Box The standard trackers begin their tracking procedure based on an initialized BBox in the first frame, including classification based [19,26,44,45], Siamese network based [9, 10, 30, 53, 59], correlation filter based [11, 12, 22, 40], and regression-based [21].”