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IEEE International Conference on Image Processing

作者： WenBei Mao Jin Zheng Bo Li Sch. of Comput. Sci. & Eng. Beihang Univ. Beijing China

ISBN: (纸本)9781479957521

This paper proposes a robust patch-based object tracking algorithm. Unlike many traditional algorithms, which divide the object into multiple patches and allocate the weight values for each patches, this paper uses SIFT feature matching to select valid patches and filter out invalid patches. The invalid patches usually corresponding to the occluded or partially transformed part of the object. Thus, guided by valid patch, patch-based color histogram provides a richer description of the object. The similarity of valid patch is used in particle filter to locate the object. Moreover, since feature similarity is easy to bring into object drift, this paper updates the object template fusing feature similarity and valid patches, which is both scale adaptive and robust to partial occlusion. The experimental results show that the proposed algorithm is more accurate and robust than state-of-the-art tracking algorithms in challenging scenarios.

关键词： image colour analysis image filtering image fusion image matching object tracking particle filtering (numerical methods) SIFT feature matching feature fusion update feature similarity fusion object template partial occlusion particle filter patch-based color histogram patch-based tracking robust patch-based object tracking algorithm valid patch selection weight value allocation Color Conferences Histograms Image color analysis Object tracking Robustness SIFT feature object update patch-based

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Accurate visual tracking via reliable patch

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VISUAL COMPUTER 2022年第2期38卷 625-638页

作者： Yang, Mengwei Lin, Yanming Huang, Detian Kong, Lingke Huaqiao Univ Coll Engn Quanzhou 362021 Peoples R China

To tackle the problem that traditional particle-filter- or correlation-filter-based trackers are prone to low tracking accuracy and poor robustness when the target faces challenges such as occlusion, rotation and scale variation in the case of complex scenes, an accurate reliable-patch-based tracker is proposed through exploiting and complementing the advantages of particle filter and correlation filter. Specifically, to cope with the challenge of continuous full occlusion, the target is divided into numerous patches by combining random with hand-crafted partition methods, and then, an effective target position estimation strategy is presented. Subsequently, according to the motion law between the patch and global target in the particle filter framework, two effective resampling rules are designed to remove unreliable particles to avoid tracking drift, and then, the target position can be estimated by the most reliable patches identified. Finally, an effective scale estimation approach is presented, in which the Manhattan distance between the reliable patches is utilized to estimate the target scale, including the target width and height, respectively. Experimental results illustrate that our tracker can not only be robust against the challenges of occlusion, rotation and scale variation, but also outperform state-of-the-art trackers for comparison in overall performance.

关键词： patch-based tracking Particle filter Correlation filter Motion trajectory Scale estimation

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Combining patch matching and detection for robust pedestrian tracking in monocular calibrated cameras

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PATTERN RECOGNITION LETTERS 2014年第0期39卷 11-20页

作者： Fuehr, Gustavo Jung, Claudio Rosito Univ Fed Rio Grande do Sul Inst Informat BR-91501970 Porto Alegre RS Brazil

This paper presents a new approach for tracking multiple people in monocular calibrated cameras combining patch matching and pedestrian detection. Initially, background removal and pedestrian detection are used in conjunction with the vertical standing hypothesis to initialize the targets with multiples patches. In the tracking step, each patch related to a given target is matched individually across frames, and their translation vectors are combined robustly with pedestrian detection results in the world coordinate frame using weighted vector median filters. Additionally, the algorithm uses the camera parameters to both estimate the person scale in a straightforward manner and to limit the search region used to track each fragment. Our experimental results indicate that our tracker can deal with occlusions and video sequences with strong appearance variations, presenting results comparable to or better than existing state-of-the-art algorithms. (C) 2013 Elsevier B.V. All rights reserved.

关键词： patch-based tracking Weighted vector median filters Homography Pedestrian tracking Pedestrian detection

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Graph-based approach to scene text localisation and tracking in videos

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ELECTRONICS LETTERS 2016年第13期52卷 1110-1111页

作者： Kim, Y. G. Koo, H. I. Ajou Univ Dept Elect & Comp Engn Suwon 443749 South Korea

A text localisation and tracking method is presented that finds text-regions in videos and assigns unique IDs to their trajectories. For the goal, a graph-based framework that can work with existing text detection methods is developed. To be precise, graphs are built where vertices are image-level text detection results and edges represent the correspondence scores of the vertices. From these graphs, text-region trajectories by using the graph-cut algorithm are extracted. This approach allows considering false positives and misses, as well as their patch-based tracking results at the same time, and text trajectories are reliably extracted. Finally, the results are refined by interpolating misses and filtering out false positives. The proposed method is submitted to the International Conference on Document Analysis and Recognition 2015 robust reading competition (video text localisation) and the method showed the best performance in terms of CLEAR MOT metrics and was ranked third place according to VACE metrics among the seven participating methods.

关键词： feature extraction graph theory image filtering interpolation natural scenes object tracking text analysis text detection video signal processing CLEAR MOT metrics International Conference on Document Analysis and Recognition 2015 VACE metrics correspondence scores false positive filtering graph vertices graph-based framework graph-cut algorithm image-level text detection interpolation patch-based tracking scene text localisation scene text tracking text-region trajectories unique ID assignment video text localisation

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patch-based Robust L1 Tracker to Dynamic Appearance Change

Patch-based Robust <i>L</i>1 Tracker to Dynamic Appearance C...

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10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Kim, Won Jin Oh, Tae-Hyun Joo, Kyungdon Kweon, In So IBM Korea Seoul South Korea Korea Adv Inst Sci & Technol Seoul South Korea

ISBN: (纸本)9781479911974;9781479911950

In this paper, we propose a robust l(1) tracking method based on a two phases sparse representation, which consists of a patch and a global appearance trackers. While recently proposed l(1) trackers showed impressive tracking accuracies, tracking the dynamic appearance is not easy to them. To overcome dynamic appearance change and achieve robust visual tracking, we model the dynamic appearance of the object by a set of local rigid patches and enhance the distinctiveness of the global appearance tracker by positive/negative learning. The integration of two approaches makes visual tracking robust to occlusion and illumination variation. We demonstrate the experiments with five challenging video sequences and compare with state-of-art trackers. We show that the proposed method successfully handle occlusion, noise, scale, illumination, and appearance change of the object.

关键词： Sparse representation l(1) minimization patch-based tracking Dynamic appearance

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