Locally Orderless Tracking

Oron, Shaul; Bar-Hillel, Aharon; Levi, Dan; Avidan, Shai

doi:10.1109/cvpr.2012.6247895

Cited by 240 publications

(96 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most stateof-the-art methods rely on solely image intensity information [17,31,8,14,35,20,7], while others employ simple color space transformations [29,27,28]. On the contrary, feature representations have been thoroughly investigated in the related fields of object recognition and action recognition [22,21].…”

Section: Related Workmentioning

confidence: 99%

“…We compare our proposed feature representation with 15 state of the art trackers: CT [35], TLD [20], DFT [31], EDFT [8], ASLA [18], L1APG [2], CSK [17], SCM [36], LOT [28], CPF [29], CXT [7], Frag [1], Struck [14], LSHT [15] and LSST [32]. Table 2 shows the comparison of our tracker with the state-of-the-art tracking methods using median DP, OP and CLE.…”

Section: Experiments 2: State Of the Art Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Coloring Channel Representations for Visual Tracking

et al. 2015

View full text Add to dashboard Cite

Abstract. Visual object tracking is a classical, but still open research problem in computer vision, with many real world applications. The problem is challenging due to several factors, such as illumination variation, occlusions, camera motion and appearance changes. Such problems can be alleviated by constructing robust, discriminative and computationally efficient visual features. Recently, biologically-inspired channel representations [9] have shown to provide promising results in many applications ranging from autonomous driving to visual tracking. This paper investigates the problem of coloring channel representations for visual tracking. We evaluate two strategies, channel concatenation and channel product, to construct channel coded color representations. The proposed channel coded color representations are generic and can be used beyond tracking. Experiments are performed on 41 challenging benchmark videos. Our experiments clearly suggest that a careful selection of color feature together with an optimal fusion strategy, significantly outperforms the standard luminance based channel representation. Finally, we show promising results compared to state-of-the-art tracking methods in the literature.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Experiments 2: State Of the Art Comparisonmentioning

confidence: 99%

Coloring Channel Representations for Visual Tracking

et al. 2015

View full text Add to dashboard Cite

show abstract

“…3. 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]. Note that all the plots are automatically generated by the code library supported by the benchmark providers.…”

Section: Experiments 1: Cvpr2013 Visual Tracker Benchmarkmentioning

confidence: 99%

Transfer Learning Based Visual Tracking with Gaussian Processes Regression

Gao

Ling

et al. 2014

Lecture Notes in Computer Science

351

288

View full text Add to dashboard Cite

Abstract. Modeling the target appearance is critical in many modern visual tracking algorithms. Many tracking-by-detection algorithms formulate the probability of target appearance as exponentially related to the confidence of a classifier output. By contrast, in this paper we directly analyze this probability using Gaussian Processes Regression (GPR), and introduce a latent variable to assist the tracking decision. Our observation model for regression is learnt in a semi-supervised fashion by using both labeled samples from previous frames and the unlabeled samples that are tracking candidates extracted from the current frame. We further divide the labeled samples into two categories: auxiliary samples collected from the very early frames and target samples from most recent frames. The auxiliary samples are dynamically re-weighted by the regression, and the final tracking result is determined by fusing decisions from two individual trackers, one derived from the auxiliary samples and the other from the target samples. All these ingredients together enable our tracker, denoted as TGPR, to alleviate the drifting issue from various aspects. The effectiveness of TGPR is clearly demonstrated by its excellent performances on three recently proposed public benchmarks, involving 161 sequences in total, in comparison with state-of-the-arts.

show abstract

“…The proposed E-RBPF model is compared to 4 state-of-the-art tracking techniques including: a) Generic RBPF tracker (G-RBPF) for multiple target tracking [34], b) a Probabilistic Data Association particle Filtering (PDAF) technique for multiple object tracking proposed in [35], c) an extended version of the context tracking (CT) algorithm proposed in [36] and c) the locally orderless tracking (LOT) from [37]. The relevance of these baseline trackers to the proposed E-RBPF model can be described as follows.…”

Section: Experiments and Analysismentioning

confidence: 99%

Autonomous detection and tracking under illumination changes, occlusions and moving camera

et al. 2015

View full text Add to dashboard Cite

ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. AbstractIn this paper, an autonomous multiple target detection and tracking technique for dynamic scenes that are influenced by illumination variations, occlusions and camera instability is proposed. The framework combines a novel DynamicReverse Analysis (DRA) approach with an Enhanced Rao-Blackwellized Particle filter (E-RBPF) for multiple target detection and tracking respectively. The DRA method, in addition to providing accurate target localization, presents the E-RBPF scheme with costs associated with the differences in intensity caused by illumination variations between consecutive frame pairs in any video of a dynamic scene. The E-RBPF inherently models these costs, thus allowing the framework to a) adapt learning parameters, b) distinguish between cameramotion and object-motion, c) deal with sample degeneracy, d) provide appropriate appearance compensation during likelihood measurement and e) handle occlusion. The proposed detect-and-track method when compared against other competing baseline techniques has demonstrated superior performance both in accuracy and robustness on challenging videos from publicly available datasets.

show abstract

Locally Orderless Tracking

Cited by 240 publications

References 26 publications

Coloring Channel Representations for Visual Tracking

Coloring Channel Representations for Visual Tracking

Transfer Learning Based Visual Tracking with Gaussian Processes Regression

Autonomous detection and tracking under illumination changes, occlusions and moving camera

Contact Info

Product

Resources

About