How to Implement Object Tracking for Computer Vision

Jacob Solawetz's guide on the Roboflow blog provides a comprehensive tutorial on implementing object tracking using custom object detection models, specifically highlighting the ease of integrating zero-shot features to simplify the process. The guide emphasizes the importance of object tracking for applications like counting distinct objects in video streams and explains how to train an object detection model using tools such as a custom YOLOv5 model or Roboflow's training solution. It details the steps to clone the zero-shot object tracking repository, set up dependencies, and process video frames using the clip_object_tracker.py script with either YOLOv5 or the Roboflow Inference API. The tutorial also offers practical examples, including tracking cars in a video, and underscores the versatility of object tracking in various fields, encouraging users to explore its potential for diverse applications.