Large Margin Object Tracking with Circulant Feature Maps

By Mengmeng Wang, Yong Liu, Zeyi Huang. Computer Vision and Pattern Recognition, 2017

573 citations recorded by the source corpus.

Structured output support vector machine (SVM) based tracking algorithms have shown favorable performance recently. Nonetheless, the time-consuming candidate sampling and complex optimization limit their real-time applications. In this paper, we propose a novel large margin object tracking method which absorbs the strong discriminative ability from structured output SVM and speeds up by the correlation filter algorithm significantly. Secondly, a multimodal target detection technique is proposed to improve the target localization precision and prevent model drift introduced by similar objects or background noise. Thirdly, we exploit the feedback from high-confidence tracking results to avoid the model corruption problem. We implement two versions of the proposed tracker with the representations from both conventional hand-crafted and deep convolution neural networks (CNNs) based features to validate the strong compatibility of the algorithm. The experimental results demonstrate that the proposed tracker performs superiorly against several state-of-the-art algorithms on the challenging benchmark sequences while runs at speed in excess of 80 frames per second.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/ece7625a346edbc5f6fab541c0c246ec06939121

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Citations in the corpus

A Twofold Siamese Network for Real-Time Object Tracking (2018, methodology): “Tracker SA-Siam BACF PTAV ECOhc DSiamM EAST Staple SiamFC CFNet LMCF LCT (ours) [17] [10] [6] [11] [15] [2] [3] [38] [34] [27]”
AutoTrack: Towards High-Performance Visual Tracking for UAV With Automatic Spatio-Temporal Regularization (2020, methodology): “, training set management [20, 24, 32], temporal restriction [14, 25], tracking confidence verification [23, 12] and over-fitting alleviation [33].”
Deep learning in video multi-object tracking: A survey (2019, methodology): “APCE was also used for an object re-identification (ReID) step, to recover from occlusions.”
Learning Dynamic Memory Networks for Object Tracking (2018, methodology): “We compare our method MemTrack with 9 recent real-time trackers (≥ 15 fps), including CFNet [30], LMCF [32], ACFN [5], RFL [36], SiamFC [4], SiamFC U [30], Staple [3], DSST [7], and KCF [15] on both OTB-2013 and OTB-2015.”
Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking (2019, methodology): “proposed a method called LMCF where the quality of response maps is verified in the learning phase and used to perform high-confidence update of appearance models [26], which reduces the learning rate to zero in low-confidence situations.”
Deep Learning for Visual Tracking: A Comprehensive Survey (2019, methodology): “These methods include the HCFT [59], DeepSRDCF [60], FCNT [61], CNNSVM [62], DPST [63], CCOT [64], GOTURN [65], SiamFC [66], SINT [67], MDNet [68], HDT [69], STCT [70], RPNT [71], DeepTrack [72], CNT [73], CF-CNN [74], TCNN [75], RDLT [76], PTAV [77], [78], CREST [79], UCT/UCTLite [80], DSiam/DSiamM”
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking (2018, methodology): “Following this work, there also exist several heuristic methods [27, 38] to address the naive model updating issues.”
Online Multi-Expert Learning for Visual Tracking (2020, unknown): “the tracking results to some extent [55].”