Effective appearance model update strategy in object tracking

Yin, Xin; Liu, Guixi

doi:10.1049/iet-cvi.2018.5091

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…To reduce the unwanted boundary effects, SRDCF [32], in which a spatial regularization component is introduced in the learning to penalize correlation filter coefficients depending on their spatial location, was proposed by Martin Danelljan et al, and they introduced a continuous-domain formulation of the DCF, called C-COT [9], and its advanced version ECO [14]. Xianglei Yin and Guixi Liu have solved the appearance model update problem by adjusting the model update rate according to APCE and its gradient [33]. Based on SRDCF, Feng Li et al proposed STRCF [18], which incorporates both temporal and spatial regularization, to solve the update problem in a closed form by temporal regularization, and it can handle boundary effects without much loss in efficiency.…”

Section: Related Workmentioning

confidence: 99%

Fast Scale Estimation Method in Object Tracking

Yin

Liu

2020

IEEE Access

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Accurate and fast scale estimation of targets is a challenging research problem in visual object tracking. Most trackers employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and it struggles when encountered with large scale variations. In this paper, a scale adaptive method is proposed, which not only improves the tracking performance, but also greatly reduces the computational costs and improves the tracking speed. Based on the scale estimation method of SAMF, the original 7 fixed scale sizes were reduced to 3, and an adaptive scale size was added. Three fixed scales were used to determine the direction of scale change, and the APCE change rate of the current frame and the previous frame was used to control another adaptive scale size. Finally, the optimal scale estimation was determined. Additionally, we investigate the update strategy to further improve the tracking accuracy. Extensive experiments on OTB50, OTB100 and VOT-ST2019 datasets demonstrate that the proposed method can tackle challenging videos well compared with baseline tracker. On OTB, we obtain a gain of 7.0% in Distance Precision, and 18.8% in Centre Location Error on the selected 43 videos with scale variation attribute, and a mean gain of 6.2% in Precision and 4.6% in Success plots on OTB50, compared with the baseline tracker SAMF. Furthermore, the proposed approach improves the tracking speed by 34% in FPS compared with SAMF. INDEX TERMS Object tracking, correlation filter, scale estimation, model update.

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Section: Related Workmentioning

confidence: 99%

Fast Scale Estimation Method in Object Tracking

Yin

Liu

2020

IEEE Access

Self Cite

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“…The ridge regression is introduced in circulant structure kernels (CSK) tracking method, and the cyclic shift is used to perform the intensive sample. However, the single grayscale feature is used in CSK method, so the feature description of the target is insufficient [39][40][41][42][43][44][45]. Based on CSK method, kernel correlation filter method (KCF) is proposed by replacing the grayscale feature with Histogram of Oriented Gradient (HOG) feature and extending the single channel into multiple channels to improve the tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Kernel correlation filter tracking strategy based on adaptive fusion response map

Xiu

2021

IET Image Processing

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Aiming at the problem that the tracking performance of the traditional kernel correlation filter tracking algorithm is easy to be affected by illumination variation, occlusion and motion blur during tracking, an improved tracking strategy is proposed. A new Histogram of Hue Gradient (HHG) feature is designed, and the new HOG-HHG feature is obtained by connecting the HOG and the HHG in series. Two features, CN and HOG-HHG, are extracted respectively, and two kernel correlation filter classifiers are constructed base on the two features above to establish the corresponding response maps of the tracking scenes, respectively. The response maps are fused adaptively to improve the tracking robustness to the complex situations in the tracking process. The updating strategy of the target model is designed based on peak sidelobe ratio (PSR) and its difference, and the adaptive thresholds are used to improve the stability of the target model. Simulation results show that the proposed method has better tracking adaptability to the illumination variation, occlusion and motion blur. Both the precision and the success rate can be enhanced. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Wang Mengmeng et al [8] used an average peak‐to‐correlation energy (APCE) to reflect the confidence level. Yin and Liu [16] used APCE threshold and gradient APCE threshold to measure tracking reliability. Ting Liu et al [9] proposed smooth constraint confidence maps (SCCM) to measure the tracking performance of different sub‐blocks.…”

Section: Introductionmentioning

confidence: 99%

Re‐detection object tracking algorithm in the cyber physical system

Huang²

2020

IET cyber-phys. syst.

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Cyber physical system (CPS) is a complex system combining computation, network and physics; object tracking is an important application of CPS. To solve the problem that the traditional kernel correlation filtering tracking algorithm cannot recover the lost object, the authors propose a re-detection object tracking algorithm. The proposed algorithm mainly designs a new adaptive detection criterion. By comparing the value of detection criterion and the value of the experience threshold, it can be judged whether the current target is lost or not. When the object tracking fails, the proposed method can generate target candidate boxes by using the edge boxes algorithm and select the best target location by applying the non-maximum suppression and the Euclidean metric methods. In addition, a fast multi-scale estimation method and an adaptive updating method are added to the tracking procedure to further improve the overall performance of the algorithm. Experimental results show that the proposed approach has a good performance in terms of precision and success rates.

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Effective appearance model update strategy in object tracking

Cited by 7 publications

References 33 publications

Fast Scale Estimation Method in Object Tracking

Fast Scale Estimation Method in Object Tracking

Kernel correlation filter tracking strategy based on adaptive fusion response map

Re‐detection object tracking algorithm in the cyber physical system

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