Deep Reinforcement Learning Based on Spatial-Temporal Context for IoT Video Sensors Object Tracking

2021 ◽  
pp. 226-235
Author(s):  
Panbo He ◽  
Chunxue Wu ◽  
Kaijun Liu ◽  
Neal N. Xiong
Keyword(s):  
Reinforcement Learning ◽  
Object Tracking ◽  
Temporal Context ◽  
Video Sensors

scholarly journals Spatio-Temporal Context, Correlation Filter and Measurement Estimation Collaboration Based Visual Object Tracking

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2841
Author(s):  
Khizer Mehmood ◽  
Abdul Jalil ◽  
Ahmad Ali ◽  
Baber Khan ◽  
Maria Murad ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Environmental Changes ◽  
Correlation Filter ◽  
Temporal Context ◽  
Visual Object ◽  
Target Model ◽  
Change Of Scale ◽  
Tracking Model ◽  
Spatio Temporal

Despite eminent progress in recent years, various challenges associated with object tracking algorithms such as scale variations, partial or full occlusions, background clutters, illumination variations are still required to be resolved with improved estimation for real-time applications. This paper proposes a robust and fast algorithm for object tracking based on spatio-temporal context (STC). A pyramid representation-based scale correlation filter is incorporated to overcome the STC’s inability on the rapid change of scale of target. It learns appearance induced by variations in the target scale sampled at a different set of scales. During occlusion, most correlation filter trackers start drifting due to the wrong update of samples. To prevent the target model from drift, an occlusion detection and handling mechanism are incorporated. Occlusion is detected from the peak correlation score of the response map. It continuously predicts target location during occlusion and passes it to the STC tracking model. After the successful detection of occlusion, an extended Kalman filter is used for occlusion handling. This decreases the chance of tracking failure as the Kalman filter continuously updates itself and the tracking model. Further improvement to the model is provided by fusion with average peak to correlation energy (APCE) criteria, which automatically update the target model to deal with environmental changes. Extensive calculations on the benchmark datasets indicate the efficacy of the proposed tracking method with state of the art in terms of performance analysis.

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

Image object tracking based on temporal context and MOSSE

2017 ◽  
Vol 20 (2) ◽  
pp. 1259-1269
Author(s):  
Ke Han
Keyword(s):  
Object Tracking ◽  
Temporal Context ◽  
Image Object

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
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