Robust multi‐feature visual tracking via multi‐task kernel‐based sparse learning

Kong, Bin; Zhu, Wei-Ping; Liang, Dong

doi:10.1049/iet-ipr.2016.1062

Cited by 13 publications

(6 citation statements)

References 41 publications

(51 reference statements)

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“…The multi‐task kernel‐based sparse learning method for multi‐feature visual tracking is proposed by Kang et al [23]. However, the algorithm estimates the kernel weights based on the pixel intensity, texture and edge.…”

Section: Related Workmentioning

confidence: 99%

Approach to model human appearance based on sparse representation for human tracking in surveillance

Damotharasamy¹

2020

IET image process

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In human tracking, sparse representation successfully localises the human in a video with minimal reconstruction error using target templates. However, the state‐of‐the‐art approaches use colour and local appearance of a human to discriminate the human from the background regions, and hence fail when the human is occluded and appears in the varying illumination environment. In this study, a robust tracking algorithm is proposed that utilises gradient orientation and fine and coarse sparse representation of the target template. Sparse representation‐based human appearance model utilises weighted gradient orientation that is insensitive to illumination variation. Coarse and fine representation of sparse code facilitates tracking under varying scales. Subspace learning from image gradient orientation is enforced with occlusion detection during the dictionary updation stage to capture the visual characteristics of the local human appearance that supports tracking under partial occlusion with lesser tracking error. The proposed human tracking algorithm is evaluated on various datasets and shows efficient human tracking performance when compared to the other state‐of‐the‐art approaches. Furthermore, the proposed human tracking algorithm is suitable for surveillance applications.

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Section: Related Workmentioning

confidence: 99%

Approach to model human appearance based on sparse representation for human tracking in surveillance

Damotharasamy¹

2020

IET image process

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“…Many tracking algorithms have been proposed over last few years, with the aim to achieve precise tracking results. The methods include traditional feature extraction based tracking [29–33] as well as deep learning based tracking [34–36]. However, there are certain factors involve in terms of aircraft tracking, that may limit the accuracy of tracking algorithms, i.e.…”

Section: Background Studymentioning

confidence: 99%

Automatic aircraft extraction using video matting and frame registration

Amin

Riaz²,

Ghafoor

2020

IET Image Processing

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“…To display and resize images, these cameras often have powerful digital signal image processors. However, a camera cannot meet the demand of object tracking applications with only a wide angle and image processing, as it lacks technologies such as: Object tracking motion detection algorithms [5][6][7], the Markov Chain research model [8][9][10][11][12], optical flow technology [13][14][15], background model approach [16,17], and kernel-based method [18][19][20][21]. These systems also use a large amount of energy.…”

Section: Object Tracking Networkmentioning

confidence: 99%

Energy-Efficient Clusters for Object Tracking Networks

Fan

2018

Energies

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Smart cities have hundreds of thousands of devices for tracking data on crime, the environment, and traffic (such as data collected at crossroads and on streets). This results in higher energy usage, as they are recording information persistently and simultaneously. Moreover, a single object tracking device, on a corner at an intersection for example has a limited scope of view, so more object tracking devices are added to broaden the view. As an increasing number of object tracking devices are constructed on streets, their efficient energy consumption becomes a significant issue. This work is concerned with decreasing the energy required to power these systems, and proposes energy-efficient clusters (EECs) of object tracking systems to achieve energy savings. First, we analyze a current object tracking system to establish an equivalent model. Second, we arrange the object tracking system in a cluster structure, which facilitates the evaluation of energy costs. Third, the energy consumption is assessed as either dynamic or static, which is a more accurate system for determining energy consumption. Fourth, we analyze all possible scenarios of the object's location and the resulting energy consumption, and derive a number of formulas for the fast computation of energy consumption. Finally, the simulation results are reported. These results show the proposed EEC is an effective way to save energy, compared with the energy consumption benchmarks of current technology.

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Robust multi‐feature visual tracking via multi‐task kernel‐based sparse learning

Cited by 13 publications

References 41 publications

Approach to model human appearance based on sparse representation for human tracking in surveillance

Approach to model human appearance based on sparse representation for human tracking in surveillance

Automatic aircraft extraction using video matting and frame registration

Energy-Efficient Clusters for Object Tracking Networks

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