RizwanMunawar/yolov7-object-tracking

YOLOv7 Object Tracking Using PyTorch, OpenCV and Sort Tracking

What it solves

This project provides a way to perform real-time object detection and tracking across video streams. It allows users to not only identify what an object is but also maintain a unique identity for that object as it moves through different frames of a video.

How it works

The system combines a detection model (primarily YOLOv7, with support for YOLOv8) to locate objects in each frame and a tracking algorithm (referencing SORT) to assign and maintain IDs for those objects. It can process inputs from various sources including video files, webcams, external cameras, and IP camera streams.

Who it’s for

This is for developers and computer vision engineers who need to implement object tracking in their applications, such as for surveillance, traffic monitoring, or any scenario where counting or following specific objects is required.

Highlights

  • Multi-Model Support: Works with YOLOv7 and YOLOv8 weights.
  • Flexible Input Sources: Supports video files, webcams, IP streams, and external cameras.
  • Detailed Output: Ability to save track centroids, object IDs, and bounding box dimensions to text files.
  • Customizable Tracking: Supports filtering by specific object classes (e.g., tracking only people) and visual aids like unique colors for different tracks.

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