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

AUTHOR(S):

Sang Gu Lee, Gi Bum Song, Yong Jun Yang

TITLE

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

ABSTRACT

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.

KEYWORDS

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

REFERENCES

[1] Georg Nebehay and Roman Pflugfelder, “Clustering of Static-Adaptive Correspondences for Deformable Object Tracking” IEEE conf. CVPR 2015.

[2] M. Kristan and et al., “A novel performance evaluation methodology for single-target trackers”, IEEE tr. PAMI 38 (11), 2016.

[3] Daniel Doyle and et al., “Optical flow background estimation for real-time pan/tilt camera object tracking”, Measurement 48, 2014.

[4] GitHub, "CMT" “https://github.com/gnebehay/CMT”

[5] Wiki "OpenCV" “https://ko.wikipedia.org/wiki/OpenCV”

[6] OpenCV team, "OpenCV" "http://opencv.org/“

[7] D. Comaniciu, V. Ramesh and P. Meer, “Kernel-based object tracking”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 5, pp.564–577, May 2003.

[8] Kari Pulli, Anatoly Baksheev, Kirill Kornyakov and Victor Eruhimov, “Real-time computer vision with OpenCV”, Communications of the ACM, 2012, 55, 61–69

[9] S. Huang; and J. Hong; , "Moving object tracking system based on CAMshift and Kalman filter," International Conference on Consumer Electronics, Communications and Networks (CECNet), pp.1423-1426, 2011.

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