Sang Gu Lee, Gi Bum Song, Yong Jun Yang



An Object Tracking for Studio Cameras by OpenCV-Based Python Program

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In this paper, we present an automatic image object tracking system for Studio cameras on the stage. For object tracking, we use the OpenCV-based Python program using PC, Raspberry Pi 3 and mobile devices. There are many methods of image object tracking such as mean-shift, CAMshift (Continuously Adaptive Mean shift), background modelling using GMM(Gaussian mixture model), template based detection using SURF(Speeded up robust features), CMT(Consensus-based Matching and Tracking) and TLD methods. CAMshift algorithm is very efficient for real-time tracking because of its fast and robust performance. However, in this paper, we implement an image object tracking system for studio cameras based CMT algorithm. This is an optimal image tracking method because of combination of static and adaptive correspondences. The proposed system can be applied to an effective and robust image tracking system for continuous object tracking on the stage in real time.


Object tracking, CMT algorithm, Studio camera, OpenCV-based Python


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Cite this paper

Sang Gu Lee, Gi Bum Song, Yong Jun Yang. (2018) An Object Tracking for Studio Cameras by OpenCV-Based Python Program. International Journal of Signal Processing, 3, 5-10


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