Adaptive Compressive Tracking based on Locality Sensitive Histograms

Chan, Sixian; Zhou, Xiaolong; Li, Junwei; Chen, Shengyong

doi:10.1016/j.patcog.2017.07.006

Cited by 11 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, now the main strategy of tracking algorithm is to build a faultless appearance model of the target through generative-based (Zhou et al, 2014;Hare et al, 2016;Ning et al, 2016;Gao et al, 2014;Zhou et al, 2013) or discriminative-based (Chan et al, 2017b(Chan et al, , 2017aZhang et al, 2017Zhang et al, , 2020Zhang et al, , 2015bZhang et al, , 2019Zhang et al, , 2015a methods and to update it effectively in subsequent trajectory estimates. However, since the appearance of the object changes differently in each frame of the image sequence, this not only makes it difficult to build a complete description of the appearance of the target object but also leads to the accumulation of errors in the tracking process.…”

Section: Introductionmentioning

confidence: 99%

Target tracking based on standard hedging and feature fusion for robot

Chan

Tao

Zhou

et al. 2021

Self Cite

View full text Add to dashboard Cite

Purpose Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of visual target tracking still has great space for improvement. This paper aims to propose an accurate visual target tracking method based on standard hedging and feature fusion. Design/methodology/approach For this study, the authors first learn the discriminative information between targets and similar objects in the histogram of oriented gradients by feature optimization method, and then use standard hedging algorithms to dynamically balance the weights between different feature optimization components. Moreover, they penalize the filter coefficients by incorporating spatial regularization coefficient and extend the Kernelized Correlation Filter for robust tracking. Finally, a model update mechanism to improve the effectiveness of the tracking is proposed. Findings Extensive experimental results demonstrate the superior performance of the proposed method comparing to the state-of-the-art tracking methods. Originality/value Improvements to existing visual target tracking algorithms are achieved through feature fusion and standard hedging algorithms to further improve the tracking accuracy of robots on targets in reality.

show abstract

Section: Introductionmentioning

confidence: 99%

Target tracking based on standard hedging and feature fusion for robot

Chan

Tao

Zhou

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, l 1 -norm constrained sparse representation (SR) has attracted increasingly more attention and applied to object tracking. [28][29][30][31][32][33][34] Despite the great success of SP in the field of tracking, less research is focused on how to establish an effective visual tracking template in the dictionary. SR requires an overcomplete template dictionary, so the linear combination of these templates can be used to approximate the estimation of new samples with very sparse coefficients.…”

Section: Introductionmentioning

confidence: 99%

Drift-free tracking via the construction of an effective dictionary

Zhao

Huang

Liu

et al. 2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Template dictionary construction is an important issue in sparse representation (SP)-based tracking algorithms. In this article, a drift-free visual tracking algorithm is proposed via the construction of an effective template dictionary. The constructed dictionary is composed of three categories of atoms (templates): nonpolluted atoms, variational atoms, and noise atoms. Moreover, the linear combinations of nonpolluted atoms are also added to the dictionary for the diversity of atoms. All the atoms are selectively updated to capture appearance changes and alleviate the model drifting problem. A bidirectional tracking process is used and each process is optimized by two-step SP, which greatly reduces the computational burden. Compared with other related works, the constructed dictionary and tracking algorithm are both robust and efficient.

show abstract

“…In order to improve the poor performance of compression tracking (CT) algorithm when the objects were occluded, Wang et al [12] proposed an improved CT algorithm based on target segmentation and feature points matching. Chan et al [13] proposed an adaptive CT algorithm that significantly improved conventional CT in four different respects. The results showed that this tracker achieved state-of-the-art performance.…”

Section: Introductionmentioning

confidence: 99%

Study of Dynamic Tracking Algorithms for Apples Under the Influence of Oscillation

Jiang

Sun

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Influenced by the force of the wind and agricultural operations, fruits often undergo oscillation, which makes it difficult to automatically monitor their growing status. It is very important to realize dynamic tracking of these oscillating fruits in order to improve automatic monitoring systems and the efficiency of picking robots. In order to investigate the accuracy of the tracking of oscillating fruits, three classic tracking algorithms were adopted and compared: the kernelized correlation filter algorithm (KCF), the compressive tracking algorithm (CT), and the multi-task tracking algorithm (MTT). The effectiveness of these algorithms was verified by testing six video sequences acquired in different environments, and three indices (the average central error, frame loss rate, and time efficiency) were used to verify their performance. The results showed that the KCF algorithm was most appropriate for the tracking of oscillating fruit objects, as it has a lower centering error and a much higher frame rate.

show abstract

Adaptive Compressive Tracking based on Locality Sensitive Histograms

Cited by 11 publications

References 11 publications

Target tracking based on standard hedging and feature fusion for robot

Target tracking based on standard hedging and feature fusion for robot

Drift-free tracking via the construction of an effective dictionary

Study of Dynamic Tracking Algorithms for Apples Under the Influence of Oscillation

Contact Info

Product

Resources

About