A real-time middle wavelength infrared multi-target detection and tracking algorithm based on LoG

2011 ◽  
Author(s):  
Song Dan ◽  
Zhao Baojun ◽  
Tang Linbo
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Tracking Algorithm ◽  
Detection And Tracking

scholarly journals A split target detection and tracking algorithm for ballistic missile tracking during the re-entry phase

2020 ◽  
Vol 16 (6) ◽  
pp. 1142-1150
Author(s):  
Muhammad Asad ◽  
Sumair Khan ◽  
Ihsanullah ◽  
Zahid Mehmood ◽  
Yifang Shi ◽  
...  
Keyword(s):  
Target Detection ◽  
Ballistic Missile ◽  
Tracking Algorithm ◽  
Detection And Tracking

scholarly journals Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zheng Zhang ◽  
Cong Huang ◽  
Fei Zhong ◽  
Bote Qi ◽  
Binghong Gao
Keyword(s):  
Machine Vision ◽  
Target Detection ◽  
Target Object ◽  
Tracking Algorithm ◽  
Moving Target ◽  
Moving Target Detection ◽  
Detection And Tracking ◽  
Swimming Motion ◽  
Behavior Tracking ◽  
And Behavior

This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an improved kernel-related filter tracking algorithm is proposed by training multiple filters, which can clearly and accurately obtain the motion trajectory of the monitored target object. Finally, it is proposed to combine the Kalman algorithm with the Camshift algorithm for optimization, which can complete the tracking and recognition of moving targets. The experimental results show that the target tracking and detection method can obtain the movement form of the template object relatively completely, and the kernel-related filter tracking algorithm can also obtain the movement speed of the target object finely. In addition, the accuracy of Camshift tracking algorithm can reach 86.02%. Results of this study can provide reliable data support and reference for expanding the application of moving target detection and tracking methods.

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