Visual target tracking based on multi-view feature fusion with online multiple instance learning

2017 ◽  
Author(s):  
Weixin Hua ◽  
Dejun Mu ◽  
Dawei Guo ◽  
Hang Liu
Keyword(s):  
Target Tracking ◽  
Visual Target ◽  
Feature Fusion ◽  
Multiple Instance Learning ◽  
Visual Target Tracking

Target tracking based on standard hedging and feature fusion for robot

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sixian Chan ◽  
Jian Tao ◽  
Xiaolong Zhou ◽  
Binghui Wu ◽  
Hongqiang Wang ◽  
...  
Keyword(s):  
Target Tracking ◽  
Visual Target ◽  
Feature Fusion ◽  
Industrial Robots ◽  
Superior Performance ◽  
Human Beings ◽  
Tracking Accuracy ◽  
Content Type ◽  
Feature Optimization ◽  
Visual Target Tracking

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.

scholarly journals Fast adaptive algorithm for visual target tracking in complex environment

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Juanting Zhou ◽  
Lixia Deng ◽  
Jason Gu ◽  
Haiying Liu ◽  
Huakang Chen
Keyword(s):  
Target Tracking ◽  
Adaptive Algorithm ◽  
Visual Target ◽  
Complex Environment ◽  
Visual Target Tracking

scholarly journals Autonomous Vehicles Navigation with Visual Target Tracking: Technical Approaches

Algorithms ◽  
2008 ◽  
Vol 1 (2) ◽  
pp. 153-182 ◽  
Author(s):  
Zhen Jia ◽  
Arjuna Balasuriya ◽  
Subhash Challa
Keyword(s):  
Target Tracking ◽  
Autonomous Vehicles ◽  
Visual Target ◽  
Visual Target Tracking

scholarly journals HAND MOVEMENT IMPROVEMENT ON VISUAL TARGET TRACKING BY MODEL-BASED COMPENSATOR

2005 ◽  
Vol 38 (1) ◽  
pp. 166-171
Author(s):  
Takenao Sugi ◽  
Junko Ide ◽  
Masatoshi Nakamura ◽  
Hiroshi Shibasaki
Keyword(s):  
Target Tracking ◽  
Visual Target ◽  
Hand Movement ◽  
Model Based ◽  
Visual Target Tracking

VISUAL TARGET TRACKING, EXTRACTION AND RECOGNITION FROM A SEQUENCE OF IMAGES USING THE LG GRAPH APPROACH

2002 ◽  
Vol 11 (04) ◽  
pp. 513-529 ◽  
Author(s):  
NIKOLAOS G. BOURBAKIS
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Target Tracking ◽  
Visual Target ◽  
Region Growing ◽  
Geometric Approach ◽  
Moving Target ◽  
Extraction Region ◽  
Fuzzy Like ◽  
Visual Target Tracking

This paper presents a methodology for visually tracking, extracting and recognizing targets from a sequence of images (video). The methodology is based on the local-global (LG) graph as a combination of algorithms, such as fuzzy-like segmentation, edge detection, thinning, region growing, fractals, feature extraction, region-graph with attributes, etc., appropriately used for tracking, extracting and recognizing targets under various conditions, such as moving target - still camera, still camera - moving target, moving target - moving camera. The main contribution of this paper is the real-time combination of algorithms that provides a human-like feedback geometric approach of processing low resolution information in a sequence of consecutive images. Simulated results of the metholodology are presented for synthetic and real images.

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