Generalized ℓP-regularized representation for visual tracking

Kong, Jun; Liu, Chen–Hua; Jiang, Min; Wu, Jiao; Tian, Shengwei; Lai, Huicheng

doi:10.1016/j.neucom.2016.03.100

Cited by 8 publications

(1 citation statement)

References 26 publications

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“…The target localization problem was assumed to be an L1 norm-related minimization problem and was resolved using convex optimization. The method was further improved [ 33 ] by proposing an lp regularization model. The lp regularization model was minimized using the accelerated proximal gradient approach, which ensured rapid convergence and less average tracking errors as compared to its predecessors [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method

Masood

Zafar

Ali

et al. 2022

Sensors

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Tracking moving objects is one of the most promising yet the most challenging research areas pertaining to computer vision, pattern recognition and image processing. The challenges associated with object tracking range from problems pertaining to camera axis orientations to object occlusion. In addition, variations in remote scene environments add to the difficulties related to object tracking. All the mentioned challenges and problems pertaining to object tracking make the procedure computationally complex and time-consuming. In this paper, a stochastic gradient-based optimization technique has been used in conjunction with particle filters for object tracking. First, the object that needs to be tracked is detected using the Maximum Average Correlation Height (MACH) filter. The object of interest is detected based on the presence of a correlation peak and average similarity measure. The results of object detection are fed to the tracking routine. The gradient descent technique is employed for object tracking and is used to optimize the particle filters. The gradient descent technique allows particles to converge quickly, allowing less time for the object to be tracked. The results of the proposed algorithm are compared with similar state-of-the-art tracking algorithms on five datasets that include both artificial moving objects and humans to show that the gradient-based tracking algorithm provides better results, both in terms of accuracy and speed.

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Section: Introductionmentioning

confidence: 99%