A novel visual tracking with occlusion detection via sparse coefficient analysis

2016 ◽  
Author(s):  
Nannan Sun ◽  
Sheng Fang ◽  
Zhe Li
Keyword(s):  
Visual Tracking ◽  
Occlusion Detection ◽  
Sparse Coefficient

scholarly journals Astronaut Visual Tracking of Flying Assistant Robot in Space Station Based on Deep Learning and Probabilistic Model

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Rui Zhang ◽  
Zhaokui Wang ◽  
Yulin Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Visual Tracking ◽  
Probabilistic Model ◽  
Color Image ◽  
Space Station ◽  
Depth Image ◽  
Single Shot ◽  
Occlusion Detection ◽  
Robust Tracking

Real-time astronaut visual tracking is the most important prerequisite for flying assistant robot to follow and assist the served astronaut in the space station. In this paper, an astronaut visual tracking algorithm which is based on deep learning and probabilistic model is proposed. Fine-tuned with feature extraction layers’ parameters being initialized by ready-made model, an improved SSD (Single Shot Multibox Detector) network was proposed for robust astronaut detection in color image. Associating the detection results with synchronized depth image measured by RGB-D camera, a probabilistic model is presented to ensure accurate and consecutive tracking of the certain served astronaut. The algorithm runs 10 fps at Jetson TX2, and it was extensively validated by several datasets which contain most instances of astronaut activities. The experimental results indicate that our proposed algorithm achieves not only robust tracking of the specified person with diverse postures or dressings but also effective occlusion detection for avoiding mistaken tracking.

scholarly journals Visual tracking using structural local DCT sparse appearance model with occlusion detection

2018 ◽  
Vol 78 (6) ◽  
pp. 7243-7266 ◽  
Author(s):  
B. K. Shreyamsha Kumar ◽  
M. N. S. Swamy ◽  
M. Omair Ahmad
Keyword(s):  
Visual Tracking ◽  
Appearance Model ◽  
Occlusion Detection

scholarly journals Target Contour Recovering for Tracking People in Complex Environments

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Jianhua Zhang ◽  
Sheng Liu ◽  
Y. F. Li ◽  
Jianwei Zhang
Keyword(s):  
Visual Tracking ◽  
Video Sequence ◽  
Tracking System ◽  
Target Object ◽  
Detection Methods ◽  
Partial Occlusion ◽  
Occlusion Detection ◽  
Challenging Problem ◽  
Complex Environments ◽  
Novel Method

Recovering people contours from partial occlusion is a challenging problem in a visual tracking system. Partial occlusions would bring about unreasonable contour changes of the target object. In this paper, a novel method is presented to detect partial occlusion on people contours and recover occluded portions. Unlike other occlusion detection methods, the proposed method is only based on contours, which makes itself more flexible to be extended for further applications. Experiments with synthetic images demonstrate the accuracy of the method for detecting partial occlusions, and experiments on real-world video sequence are also carried out to prove that the method is also good enough to be used to recover target contours.

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