An improved kernelized-correlation-filter spatial target tracking method using variable regularization and spatio-temporal context model

2020 ◽  
Author(s):  
YuXuan Mao ◽  
ZhiJia Yang ◽  
XiaoZheng Liu ◽  
TingHua Zhang ◽  
Kun Gao
Keyword(s):  
Target Tracking ◽  
Correlation Filter ◽  
Temporal Context ◽  
Context Model ◽  
Tracking Method ◽  
Spatio Temporal

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.

scholarly journals Robust spatio-temporal context for infrared target tracking

2018 ◽  
Vol 91 ◽  
pp. 263-277 ◽  
Author(s):  
Zheng Cui ◽  
Jingli Yang ◽  
Shouda Jiang ◽  
Junbao Li ◽  
Yanfeng Gu
Keyword(s):  
Target Tracking ◽  
Temporal Context ◽  
Spatio Temporal

scholarly journals Robust Online Learned Spatio-Temporal Context Model for Visual Tracking

2014 ◽  
Vol 23 (2) ◽  
pp. 785-796 ◽  
Author(s):  
Longyin Wen ◽  
Zhaowei Cai ◽  
Zhen Lei ◽  
Dong Yi ◽  
Stan Z. Li
Keyword(s):  
Visual Tracking ◽  
Temporal Context ◽  
Context Model ◽  
Spatio Temporal

scholarly journals Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit

2018 ◽  
Vol 8 (10) ◽  
pp. 1772 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Yiwen Liu ◽  
Chao Tan
Keyword(s):  
Thermal Image ◽  
Support Vector ◽  
Temporal Context ◽  
Accurate Identification ◽  
Thermal Images ◽  
Tracking Method ◽  
Online Identification ◽  
Spatio Temporal ◽  
Infrared Thermal Images ◽  
Cutting Unit

In order to achieve accurate identification of a shearer cutting state, infrared thermal images were creatively adopted in this paper. As the position of a shearer cutting unit is constantly changing, and the temperature in the vicinity is obviously distinct, mathematical morphology theory was used to detect the cutting unit in an infrared thermal image. Furthermore, a target tracking method is put forward to achieve cutting unit tracking based on the combination of morphology and a spatio-temporal context (STC) algorithm. Then, the temperature field features of this tracking area were extracted, and an intelligent classifier based on a support vector machine (SVM) was constructed to identify the cutting state of the shearer. Some experiments are presented, and the results indicate the feasibility and superiority of the proposed method.

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