Color Based Object Detection And Tracking

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Color Based Object Detection And Tracking

Platform : Python

Delivery Duration : 3-4 working Days

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SKU: Color Based Object Detection And Tracking Categories: ,



This approach is based on the application of a colour based object detection and detection technique, a method for HSV, and the key points matching technique.


This paper, introduces a technique for automating the methodology of detecting and tracking objects utilizing color feature and motion. Video Tracking is the methodology of finding a moving object over the long distance using a camera. The main aim of video tracking is to relate target objects in consecutive video frames. The relationship can be especially troublesome when the objects are moving speedy with respect to the frame rate. An interchange situation that grows the unpredictability of the issue is the time when the tracking object changes orientation after eventually. For these circumstances video tracking frameworks typically utilize a movement model which depicts how the image of the target may change for distinctive conceivable movements of the object. In this paper an algorithm is developed to track the real-time moving objects in different frames of a video using color feature and motion.


  • Threshold based segmentation
  • Gaussian mixture model
  • Color histogram and gradient based segmentation


  • It is sensitive illumination changes leads to more background noises
  • It is time consuming process
  • It is not provides better results due to varying light conditions, shadows and other occlusions


In this proposed system the video is converted into frames and then it is get divided into sub bands and then background is get subtracted, then the object is get identified and then it is tracked in the framed from the video.




  • Video surveillance
  • Machine Vision systems
  • Object Recognition


  • Python
  • Opencv
  • Numpy


[I] Zhengjie Wang, YuanZhao, JifenZhang, YinjingGuo, “Research on Motion Detection of Video Surveillance System”, presented at 3rd International Conference on Image and Signal Processing, Yantai, China, Oct 16-18,2010, pp. 193-197.

[2] RupaJiS.Rakibe, BharatiD.Patil, “Background Subtraction Algorithm Based Human Motion Detection”, International Journal of SCientific and Research Publications, vol. 3, no. 5, pp. 1-4, May 2009.

[3] K.Kavitha, ATejaswini, “Background Detection and Subtraction for Image Sequences in Video”, International Journal of Computer Science and Information Technologies, vol. 3, no. 5, pp. 5223-5226, Oct 2012.

[4] M. H. Ali, Fadhan Hafiz, A AShafie, “Motion Detection Techniques using Optical Flow”, International Scholarly and Scientific Research and Innovation, vol. 3, no.8, pp. 503-508, Sept 2009.

[5] Wei Shuigen, Chen Zhen, Dong Hua,”Motion Detection Based on Temporal Difference Method and Optical Flow field”, presented at International Symposium on Electronic Commerce and Security, Nanchang, China, May 22-24,2009, pp. 85-89.

[6] K. Suganya Devi, N. Malmurugan, M. Manikandan, “Object Motion Detection in Video Frames Using Background Frame Matching”, International Journal of Computer Trends and Technology, vol.4, no. 6, pp. 1928-1931, June 2013.

[7] Nan Lu, Jihong Wang, Q.H Wu, Li Yang, “An Improved Motion Detection Method for Real-Time Surveillance”, International Journal of Computer Science, vol. 35, no. I, Feb 2008.


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