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

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.

[1]  Roman P. Pflugfelder,et al.  Clustering of static-adaptive correspondences for deformable object tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Kari Pulli,et al.  Real-time computer vision with OpenCV , 2012, Commun. ACM.

[3]  Jiri Matas,et al.  A Novel Performance Evaluation Methodology for Single-Target Trackers , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Alan L. Jennings,et al.  Optical Flow Background Estimation for Real-time Pan/tilt Camera Object Tracking , 2014 .

[6]  Jingxin Hong,et al.  Moving object tracking system based on camshift and Kalman filter , 2011, 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet).