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PROJECT 04 / AI / COMPUTER VISION / VIDEO ANALYTICS

Motion in focus.

Video-Based Motion Detection & Object Tracking System.

Vehicle detections and tracking IDs annotated on a video frame
ANNOTATED VIDEO FRAME / DETECTION BOXES AND TRACKING IDS
ABOUT THE WORK / 04

From idea to implementation.

Developed a computer-vision application to detect and track vehicles in video footage. The system uses YOLO11 and ByteTrack, with OpenCV motion detection and a Flask-based interface for video uploads, processing, and annotated output visualization.

Key features & contributions

  • Detects and tracks vehicles in video footage using YOLO11 and ByteTrack.
  • Uses OpenCV motion detection to identify movement across video frames.
  • Provides a Flask web interface for video upload, processing, and annotated output videos.
  • Generates object tracking IDs and detection metrics to support video analysis and monitoring.
  • Integrates video processing and output generation for surveillance-video analysis.
TECHNOLOGIES / TOOLS
PythonOpenCVYOLO11ByteTrackFlaskPyTorchHTMLCSSJavaScript
VIEW GITHUB REPOSITORY ↗
AI
EVIDENCE / SOURCE ACCURACY

Real work. Clear context.

The screenshot is the tracking output image included next to this project in your portfolio document. No separate hosted demo/video URL was supplied.

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