https://github.com/kmr0877/realtime-object-tracking-using-opencv

Image segmentation, feature description and object tracking form the foundation of many successful applications of computer vision. The objective of this task is for you to become familiar with these techniques and their implementation in OpenCV.
https://github.com/kmr0877/realtime-object-tracking-using-opencv

contours cv2 feature-detection feature-extraction ffmpeg ffmpeg-wrapper grayscale-images imageio imageio-framework numpy numpy-arrays numpy-library object-detection object-tracking objectmapper opencv pylab python3 shutil video-processing

Last synced: 3 months ago
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Image segmentation, feature description and object tracking form the foundation of many successful applications of computer vision. The objective of this task is for you to become familiar with these techniques and their implementation in OpenCV.

• Host: GitHub
• URL: https://github.com/kmr0877/realtime-object-tracking-using-opencv
• Owner: kmr0877
• License: mit
• Created: 2017-08-28T16:56:48.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2017-10-23T13:58:27.000Z (about 8 years ago)
• Last Synced: 2025-04-12T21:33:47.742Z (8 months ago)
• Topics: contours, cv2, feature-detection, feature-extraction, ffmpeg, ffmpeg-wrapper, grayscale-images, imageio, imageio-framework, numpy, numpy-arrays, numpy-library, object-detection, object-tracking, objectmapper, opencv, pylab, python3, shutil, video-processing
• Language: Jupyter Notebook
• Size: 68.2 MB
• Stars: 7
• Watchers: 0
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: License.md

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README

          
# Object-Tracking-using-Opencv

Image segmentation, feature description and object tracking form the foundation of many successful applications of computer vision. The objective of this task is for you to become familiar with these techniques and their implementation in OpenCV.

Given a video with several (up to 3) objects of interest, the tasks are to detect the objects and track them through the video.

Uses feature descriptors to match objects in the model frame to those in each frame (image) of the sequence.

To display the estimated object locations in each frame in the video.

Display the ongoing object trajectories, based on the estimated object locations in each frame in the video.

## _Sample Interaction _

https://www.youtube.com/embed/8VDF_qqcq3M