Context tracker: Exploring supporters and distracters in unconstrained environments

By Thang Ba Dinh, Nam S. Vo, Gérard G. Medioni. Computer Vision and Pattern Recognition, 2011

559 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a1edc13e5c2517b57d432f4a6c6b693152e8c4bb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Mobile Augmented Reality Survey: From Where We Are to Where We Go (2017, unknown): “[227], [228], [229], [230], [231], [232], and [233].”
DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking (2014, methodology): “We compare our method with 9 other state-of-the-art trackers including TLD [12], CXT [6], ASLA [11], Struck [9], SCM [19], and the classical tracking algorithms, e.g., Frag [1], CPF [15], IVT [16] and MIL [2].”
Real-time part-based visual tracking via adaptive correlation filters (2015, methodology): “We compared the proposed algorithm with thirteen state-of-the-art visual trackers: Frag[1], TLD [21], IVT [34], DFT [35], ORIA [44], LOT [32], CXT [13], L1apg [5], MTT [53], ASLA [20], MIL [4], Struck [16] and KCF [17].”
A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration (2014, methodology): “Precision plots of OPE − out−of−plane rotation (39) SAMF [0.754] KCF [0.729] CN [0.652] VTD [0.620] SCM [0.618] VTS [0.604] Struck [0.597] TLD [0.596] CXT [0.574] CSK [0.540] (h) Fig.”
Part-based multiple-person tracking with partial occlusion handling (2012, background): “On the other hand, discriminative tracking approaches with online learning have flourished recently [1, 2, 11, 6, 10, 8].”
Hedged Deep Tracking (2016, methodology): “Comparison with state-of-the-art trackers We compare our algorithm with 8 recent state-of-theart trackers: DLT [30], CNN-SVM [18], KCF [17], MEEM [36], Struck [14], CXT [8], TLD [20], and SCM [41].”
Object Tracking Benchmark (2015, background): “Recently some approaches have been proposed by mining auxiliary objects or lo-cal visual information surrounding the target to assist tracking [59, 24, 18].”
MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking (2015, background): “Some long-term tracking approaches [8, 23, 29, 41, 44] have also been proposed with promising results.”
Discriminative Correlation Filter Tracker with Channel and Spatial Reliability (2016, unknown): “2012), CXT (Dinh et al. 2011), 0 10 20 30 40 50 Location error threshold 0 0.”
Context-Aware Correlation Filter Tracking (2017, methodology): “Lastly, we include TLD [13] and the classic context tracker CXT [7] for reference.”
MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization (2014, background): “Due to limited space, only the results of the top five trackers are reported in each plot (SPLTT [23], Struck [11], SCM [32], TLD [14], VTD [16], VTS [17], ASLA [13] and CXT [6]).”
Track and Segment: An Iterative Unsupervised Approach for Video Object Proposals (2016, methodology): “We first evaluate the quality of our spatio-temporal box proposals against tracking algorithms on the Visual Tracker SCM [59] Struck [20] TLD [26] ASLA [25] CXT [12] VTD [30] VTS [31] TGPR [17] RPT [34] [52] [35] We compare our unsupervised video object box proposals to state-of-the-art supervised t”
Online Object Tracking: A Benchmark (2013, background): “When an object moves fast, dense sampling based trackers (e.g., Struck, TLD and CXT) perform much better than others.”
Visual Tracking: an Experimental Survey (2013, unknown): “Also confusion with similar objects in the scene is studied recently [90].”
Transfer Learning Based Visual Tracking with Gaussian Processes Regression (2014, methodology): “These trackers include Struck [8], SCM [45], TLD [15], ASLA [14], VTD [17], VTS [18], CXT [4], LSK [24], CSK [10], MTT [44] and LOT [27].”
Unsupervised incremental learning of deep descriptors from video streams (2017, background): “Only a few works on tracking have reported drift-free results on on very long video sequences ( [18, 19, 10, 20, 21] among the few), and only few of them have provided convincing evidence on the possibility of incremental learning strategies that are asymptotically stable [18] [10].”
In defense of color-based model-free tracking (2015, background): “Supporting regions as used by [15, 19] have different appearance than the target but cooccur with it, providing valuable cues to overcome occlusions.”
Discriminative Scale Space Tracking (2016, methodology): “The trackers used for our comparison are: MIL [18], IVT [27], CT [17], TLD [28], DFT [6], EDFT [29], ASLA [5], L1APG [14], CSK [1], SCM [30], LOT [31], CPF [32], CXT [33], Frag [34], Struck [3], LSHT [4], LSST [35], KCF [9] and SAMF [23].”
Fast Visual Tracking via Dense Spatio-temporal Context Learning (2014, methodology): “To ﬁnd consistent regions, key points surrounding the object are ﬁrst extracted to help locating the object position in [13,9,25].”