Approximate Proximal Gradient-Based Correlation Filter for Target Tracking in Videos: A Unified Approach

Masood, Haris; Rehman, Saad; Khan, Abdullah; Riaz, Farhan; Hassan, Ali; Abbas, Muhammad

doi:10.1007/s13369-019-03861-3

Cited by 8 publications

(9 citation statements)

References 32 publications

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“…The fifth data set utilized is called “Singer” [ 13 ], and it contains aphotographs of a singer performing in a concert. The dataset is deemed significant since the pictures are continually zoomed in and out, posing a challenge to the object tracking system, as illustrated in Figure 12 .…”

Section: Resultsmentioning

confidence: 99%

“…Training tracking algorithms, such as approximate proximal gradient methods [ 13 ] and rapid gradient descent [ 14 ], is, in general, a very complex optimization issue because it involves a large number of secondary variables. Depending on the datasets and the problem at hand, the goal is to implement a tracking routine that provides faster results compared to its predecessors [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

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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: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Masood

Zafar

Ali

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ma et al [32] proposed a long-term correlation filter tracker (LCT) which decomposed the tracking problem into estimation of translation and scale, and redetects the target by online training of a random fern classifier. Masood et al [33] proposed tracking framework which uses a maximum average correlation height (MACH) filter for detection and proximal gradient algorithm-based particle filter for tracking.…”

Section: Related Workmentioning

confidence: 99%

Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking

et al. 2020

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Object tracking is still an intriguing task as the target undergoes significant appearance changes due to illumination, fast motion, occlusion and shape deformation. Background clutter and numerous other environmental factors are other major constraints which remain a riveting challenge to develop a robust and effective tracking algorithm. In the present study, an adaptive Spatio-temporal context (STC)-based algorithm for online tracking is proposed by combining the context-aware formulation, Kalman filter, and adaptive model learning rate. For the enhancement of seminal STC-based tracking performance, different contributions were made in the proposed study. Firstly, a context-aware formulation was incorporated in the STC framework to make it computationally less expensive while achieving better performance. Afterwards, accurate tracking was made by employing the Kalman filter when the target undergoes occlusion. Finally, an adaptive update scheme was incorporated in the model to make it more robust by coping with the changes of the environment. The state of an object in the tracking process depends on the maximum value of the response map between consecutive frames. Then, Kalman filter prediction can be updated as an object position in the next frame. The average difference between consecutive frames is used to update the target model adaptively. Experimental results on image sequences taken from Template Color (TC)-128, OTB2013, and OTB2015 datasets indicate that the proposed algorithm performs better than various algorithms, both qualitatively and quantitatively.

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“…The proposed method include a symmetrical Gaussian filter label to locate the position of the target in the subsequent frame of the video. The Gaussian filter label used is symmetrical shape similar to maxican hat [52,53] shown in the Figure 1f-g. Lett = F (t) andŷ = F (y), where F (.) represent the Discrete Fourier Transformation of the multi-channel features.…”

Section: Fusion and Reductionmentioning

confidence: 99%

Low-Rank Multi-Channel Features for Robust Visual Object Tracking

et al. 2019

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Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), Robustness-Driven Hybrid Descriptor (RDHD), Histogram of Oriented Gradients (HoG), and Color Naming (CN) features. We reduced the rank of our proposed multi-channel feature to diminish the computational complexity. We formulated the Support Vector Machine (SVM) model by utilizing the circulant matrix of our proposed feature vector in the kernel correlation filter. The use of discrete Fourier transform in the iterative learning of SVM reduced the computational complexity of our proposed visual tracking algorithm. Extensive experimental results on Visual Tracker Benchmark dataset show better accuracy in comparison to other state-of-the-art trackers.

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Approximate Proximal Gradient-Based Correlation Filter for Target Tracking in Videos: A Unified Approach

Cited by 8 publications

References 32 publications

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

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

Context-Aware and Occlusion Handling Mechanism for Online Visual Object Tracking

Low-Rank Multi-Channel Features for Robust Visual Object Tracking

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