Real-Time Visual Object Tracking Based on Reinforcement Learning with Twin Delayed Deep Deterministic Algorithm

2019 ◽  
pp. 165-177
Author(s):  
Shengjie Zheng ◽  
Huan Wang
Keyword(s):  
Reinforcement Learning ◽  
Object Tracking ◽  
Real Time ◽  
Deterministic Algorithm ◽  
Visual Object ◽  
Visual Object Tracking

Robust real-time visual object tracking via multi-scale fully convolutional Siamese networks

2018 ◽  
Vol 77 (17) ◽  
pp. 22131-22143 ◽  
Author(s):  
Longchao Yang ◽  
Peilin Jiang ◽  
Fei Wang ◽  
Xuan Wang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Multi Scale ◽  
Siamese Networks

Action-Driven Visual Object Tracking With Deep Reinforcement Learning

2018 ◽  
Vol 29 (6) ◽  
pp. 2239-2252 ◽  
Author(s):  
Sangdoo Yun ◽  
Jongwon Choi ◽  
Youngjoon Yoo ◽  
Kimin Yun ◽  
Jin Young Choi
Keyword(s):  
Reinforcement Learning ◽  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking

A real-time visual object tracking system based on Kalman filter and MB-LBP feature matching

2014 ◽  
Vol 75 (4) ◽  
pp. 2393-2409 ◽  
Author(s):  
Zebin Cai ◽  
Zhenghui Gu ◽  
Zhu Liang Yu ◽  
Hao Liu ◽  
Ke Zhang
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Real Time ◽  
Feature Matching ◽  
Tracking System ◽  
Visual Object ◽  
Visual Object Tracking

scholarly journals Parallel Correlation Filters for Real-Time Visual Tracking

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2362 ◽  
Author(s):  
Yijin Yang ◽  
Yihong Zhang ◽  
Demin Li ◽  
Zhijie Wang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Research Field ◽  
Tracking Performance ◽  
Correlation Filter ◽  
Visual Object ◽  
Correlation Filters ◽  
Illumination Variation ◽  
Visual Object Tracking ◽  
Appearance Changes

Correlation filter-based methods have recently performed remarkably well in terms of accuracy and speed in the visual object tracking research field. However, most existing correlation filter-based methods are not robust to significant appearance changes in the target, especially when the target undergoes deformation, illumination variation, and rotation. In this paper, a novel parallel correlation filters (PCF) framework is proposed for real-time visual object tracking. Firstly, the proposed method constructs two parallel correlation filters, one for tracking the appearance changes in the target, and the other for tracking the translation of the target. Secondly, through weighted merging the response maps of these two parallel correlation filters, the proposed method accurately locates the center position of the target. Finally, in the training stage, a new reasonable distribution of the correlation output is proposed to replace the original Gaussian distribution to train more accurate correlation filters, which can prevent the model from drifting to achieve excellent tracking performance. The extensive qualitative and quantitative experiments on the common object tracking benchmarks OTB-2013 and OTB-2015 have demonstrated that the proposed PCF tracker outperforms most of the state-of-the-art trackers and achieves a high real-time tracking performance.

scholarly journals Fully Convolutional Single-Crop Siamese Networks for Real-Time Visual Object Tracking

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1084 ◽  
Author(s):  
Dong-Hyun Lee
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Multiple Scale ◽  
Visual Object ◽  
Feature Maps ◽  
Visual Object Tracking ◽  
Performance Improvements ◽  
Time Operation ◽  
Real Time Operation ◽  
Speed Performance

The visual object tracking problem seeks to track an arbitrary object in a video, and many deep convolutional neural network-based algorithms have achieved significant performance improvements in recent years. However, most of them do not guarantee real-time operation due to the large computation overhead for deep feature extraction. This paper presents a single-crop visual object tracking algorithm based on a fully convolutional Siamese network (SiamFC). The proposed algorithm significantly reduces the computation burden by extracting multiple scale feature maps from a single image crop. Experimental results show that the proposed algorithm demonstrates superior speed performance in comparison with that of SiamFC.

Investigation of Threading Building Blocks Framework on Real Time Visual Object Tracking Algorithm

2014 ◽  
Vol 666 ◽  
pp. 240-244
Author(s):  
M.C. Ang ◽  
Elankovan Sundararajan ◽  
K.W. Ng ◽  
Amirhossein Aghamohammadi ◽  
T.L. Lim
Keyword(s):  
Image Processing ◽  
Object Tracking ◽  
Real Time ◽  
Building Blocks ◽  
Search And Rescue ◽  
Tracking Algorithm ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Core System ◽  
And Robotics

Object tracking plays important roles in various applications such as surveillance, search and rescue, augmented reality and robotics. This paper presents an investigation on multi-threading framework capability for color-based object tracking applications. A multi-threading framework based on Threading Building Blocks (TBB) was implemented on a multi-core system to enhance the image processing performance on a real-time visual object tracking algorithm. Intel Parallel Studio was used to implement this parallel framework. The performance between sequential and multi-threading framework was evaluated and compared. We demonstrated the multi-threading framework was approximately two times faster when compared to the sequential framework in our experiments.

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