Real-Time Compressive Tracking

By Kaihua Zhang, Lei Zhang, Ming-Hsuan Yang. European Conference on Computer Vision, 2012

1,557 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9d57723b4908397654fb1846d37db403d8b2b56a

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In Computer Vision and Pattern Recognition

Citations in the corpus

Multi-target tracking by learning local-to-global trajectory models (2015, neutral): “As explained in [49], the conditional distributions pðvijy1⁄4 1Þ and pðvijy1⁄4 0Þ in the classifier HðvÞ are assumed to be Gaussian distributed with four parameters ðμ(1)i ;σ(1)i ;μi ;σi Þ where pðvijy1⁄4 1Þ N ðμ(1)i ;σ(1)i Þ; pðvijy1⁄4 0Þ N ðμi ;σi Þ; ð13Þ”
A Novel Performance Evaluation Methodology for Single-Target Trackers (2015, methodology): “The effect of the burn-in period was further quantified by running several state-of-the-art trackers STRUCK [72], DSST [74], SAMF ([75]) and KCF [73] and two trackers commonly used as baselines, CT [69] and FRT [57] on the VOT2014 dataset.”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “A total of 278 frames were used and results are obtained with the tracking by detection method,10 the scene flow-based method [157], an L1-based tracking method [158], and a compressive method [159].”
Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices (2015, background): “Object tracking and scene change detection: There are many existing tracking algorithms [60, 17] and scene change detection techniques [39, 63].”
Learning a Deep Compact Image Representation for Visual Tracking (2013, methodology): “These trackers are: MTT [26], CT [24], VTD [15], MIL [3], a latest variant of L1T [4], TLD [13], and IVT [18].”
A survey of appearance models in visual object tracking (2013, neutral): “…and Suter 2007] — — Nonlinear subspace learning Local linear embedding Kernel principle component analysis 14 [Mei and Ling 2009; Li et al. 2011] [Zhang et al. 2012; Jia et al. 2012] [Bao et al. 2012] — — Sparse representation 1 sparse approximation 15 [Li et al. 2012] — —…”
Transfer Learning Based Visual Tracking with Gaussian Processes Regression (2014, methodology): “ii) Some studies [7,41,45,19] notice that appearance models are often updated with noisy and potentially misaligned samples, which often leads to drifting.”
LIME: Low-Light Image Enhancement via Illumination Map Estimation (2017, background): “U NDOUBTEDLY, high-visibility images reflect clear details of target scenes, which are critical to many visionbased techniques, such as object detection [1] and tracking [2].”
A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration (2014, background): “Tracking-by-detection trackers [11] [1] [16] [34] are very popular due to its high performance and efficiency.”
Adaptive Color Attributes for Real-Time Visual Tracking (2014, methodology): “The discriminative approaches [7, 27, 5, 9] aim at differentiating the target from”
Real-time part-based visual tracking via adaptive correlation filters (2015, methodology): “Generative methods treat tracking problem as searching for the regions which are the most similar to the tracked targets [3, 4, 6, 22, 24, 26– 28, 46, 52].”
Robust Visual Tracking via Convolutional Networks Without Training (2015, background): “M.-H. Yang is with the School of Engineering, University of California at Merced, Merced, CA 95344 USA (e-mail: mhyang@ucmerced.edu).”
Tracking via Robust Multi-task Multi-view Joint Sparse Representation (2013, neutral): “htm[34]; http://www.”
Learning Adaptive Discriminative Correlation Filters via Temporal Consistency Preserving Spatial Feature Selection for Robust Visual Object Tracking (2018, background): “Recently, trackers based on sparse and low-rank representations had also achieved robust results by considering structural constraints and manifold information [24], [25], [26], [27], [28].”
Learning Spatially Regularized Correlation Filters for Visual Tracking (2015, methodology): “We provide a comparison of our tracker with 24 state-ofthe-art methods from the literature: MIL [2], IVT [28], CT [36], TLD [22], DFT [29], EDFT [12], ASLA [21], L1APG [3], CSK [19], SCM [37], LOT [26], CPF [27], CXT [11], Frag [1], Struck [16], LSHT [17], LSST [32], ACT [10], KCF [20], CFLB [14], D”
Tracking Revisited Using RGBD Camera: Unified Benchmark and Baselines (2013, methodology): “We also evaluated six state-of-theart RGB trackers: TLD[15], CT[30], MIL[3], semi-B[11], Struck[12] and VTD[16], since currently there is no other public available RGBD tracking algorithms for general objects.”
Sparse coding based visual tracking: Review and experimental comparison (2013, methodology): “In addition, we also select ﬁve baseline trackers, Frag [60], IVT [4], MIL [13], OAB [11] and CT [55] for comparison.”
Long-term correlation tracking (2015, methodology): “Struck [9], CT [29], and ASLA [13]); and (iii) tracking by multiple online classifiers (TLD [14], SCM [30], MEEM [27], and TGPR [8]).”
In defense of color-based model-free tracking (2015, methodology): “Furthermore, we include CT [47], DFT [39], FoT [43], HT [17], IVT [38], LGT [42], MIL [4], and TLD [25].”