Collaborative Deep Reinforcement Learning for Multi-object Tracking

2018 ◽  
pp. 605-621 ◽  
Author(s):  
Liangliang Ren ◽  
Jiwen Lu ◽  
Zifeng Wang ◽  
Qi Tian ◽  
Jie Zhou
Keyword(s):  
Reinforcement Learning ◽  
Object Tracking

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

scholarly journals End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning

2020 ◽  
Vol 42 (6) ◽  
pp. 1317-1332 ◽  
Author(s):  
Wenhan Luo ◽  
Peng Sun ◽  
Fangwei Zhong ◽  
Wei Liu ◽  
Tong Zhang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Object Tracking ◽  
Real World ◽  
Active Object ◽  
End To End

scholarly journals A Novel Motion-Intelligence-Based Control Algorithm for Object Tracking by Controlling PAN-Tilt Automatically

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Jianbing Yang ◽  
Zhiyong Tang ◽  
Zhongcai Pei ◽  
Xiao Song
Keyword(s):  
Reinforcement Learning ◽  
Object Tracking ◽  
Motion Control ◽  
Intelligent Control ◽  
Control Algorithm ◽  
Angular Speed ◽  
Control Model ◽  
Tracking Accuracy ◽  
Camera Systems ◽  
Center Point

As computer vision develops, pan-tilt platform visual systems are able to track moving target over static camera systems. In this paper, a novel motion-intelligence-based control algorithm for object tracking by controlling pan-tilt platform has been proposed. The algorithm includes the motion control model based on angular speed and the intelligent control algorithm based on reinforcement learning (RL). The motion control model converts deviation between the center point of the tracked target and the center point of the frame to angular speed of pan-tilt platform. It can keep position of the tracked object in the center of the frame automatically. The intelligent control algorithm based on reinforcement learning can reduce the error between the ideal value and the actual value when the pan-tilt platform moves. The two blocks work together to make the pan-tilt platform track a dynamic object more stably and the experiment result shows that both the tracking accuracy and robustness are improved.

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