Visual object tracking combined normal hedge and kernel sparse representation classification

2012 ◽  
Vol 20 (11) ◽  
pp. 2540-2547 ◽  
Author(s):  
匡金骏 KUANG Jin-jun ◽  
柴毅 CHAI Yi ◽  
熊庆宇 XIONG Qing-yu
Keyword(s):  
Sparse Representation ◽  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Sparse Representation Classification

scholarly journals Visual Object Tracking using Sparse Representation and Interest Points in a Double Step Approach

2020 ◽  
Vol 175 (10) ◽  
pp. 1-9
Author(s):  
Mohamad Hosein Davoodabadi Farahani ◽  
Mohsen Khan Mohamadi ◽  
Mojtaba Lotfizad
Keyword(s):  
Sparse Representation ◽  
Object Tracking ◽  
Visual Object ◽  
Interest Points ◽  
Double Step ◽  
Visual Object Tracking

Visual object tracking via sample-based Adaptive Sparse Representation (AdaSR)

2011 ◽  
Vol 44 (9) ◽  
pp. 2170-2183 ◽  
Author(s):  
Zhenjun Han ◽  
Jianbin Jiao ◽  
Baochang Zhang ◽  
Qixiang Ye ◽  
Jianzhuang Liu
Keyword(s):  
Sparse Representation ◽  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking

scholarly journals Visual Object Tracking Using Structured Sparse PCA-Based Appearance Representation and Online Learning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3513 ◽  
Author(s):  
Gang-Joon Yoon ◽  
Hyeong Hwang ◽  
Sang Yoon
Keyword(s):  
Sparse Representation ◽  
Object Tracking ◽  
Intelligent Systems ◽  
Principal Component ◽  
Research Area ◽  
Target Object ◽  
Visual Object ◽  
Appearance Modeling ◽  
Sparse Principal Component Analysis ◽  
Visual Object Tracking

Visual object tracking is a fundamental research area in the field of computer vision and pattern recognition because it can be utilized by various intelligent systems. However, visual object tracking faces various challenging issues because tracking is influenced by illumination change, pose change, partial occlusion and background clutter. Sparse representation-based appearance modeling and dictionary learning that optimize tracking history have been proposed as one possible solution to overcome the problems of visual object tracking. However, there are limitations in representing high dimensional descriptors using the standard sparse representation approach. Therefore, this study proposes a structured sparse principal component analysis to represent the complex appearance descriptors of the target object effectively with a linear combination of a small number of elementary atoms chosen from an over-complete dictionary. Using an online dictionary for learning and updating by selecting similar dictionaries that have high probability makes it possible to track the target object in a variety of environments. Qualitative and quantitative experimental results, including comparison to the current state of the art visual object tracking algorithms, validate that the proposed tracking algorithm performs favorably with changes in the target object and environment for benchmark video sequences.

scholarly journals Adaptive Channel Selection for Robust Visual Object Tracking with Discriminative Correlation Filters

2021 ◽  
Author(s):  
Tianyang Xu ◽  
Zhenhua Feng ◽  
Xiao-Jun Wu ◽  
Josef Kittler
Keyword(s):  
Object Tracking ◽  
Augmented Lagrangian Method ◽  
Channel Selection ◽  
Image Feature ◽  
Superior Performance ◽  
Appearance Model ◽  
Visual Object ◽  
Correlation Filters ◽  
Visual Object Tracking ◽  
Feature Representations

AbstractDiscriminative Correlation Filters (DCF) have been shown to achieve impressive performance in visual object tracking. However, existing DCF-based trackers rely heavily on learning regularised appearance models from invariant image feature representations. To further improve the performance of DCF in accuracy and provide a parsimonious model from the attribute perspective, we propose to gauge the relevance of multi-channel features for the purpose of channel selection. This is achieved by assessing the information conveyed by the features of each channel as a group, using an adaptive group elastic net inducing independent sparsity and temporal smoothness on the DCF solution. The robustness and stability of the learned appearance model are significantly enhanced by the proposed method as the process of channel selection performs implicit spatial regularisation. We use the augmented Lagrangian method to optimise the discriminative filters efficiently. The experimental results obtained on a number of well-known benchmarking datasets demonstrate the effectiveness and stability of the proposed method. A superior performance over the state-of-the-art trackers is achieved using less than $$10\%$$ 10 % deep feature channels.

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