Real-time cattle tracking and counting system

Project Description: We are looking for a freelance computer vision developer to design a real-time cattle tracking and counting system using a fixed camera installed in a livestock building. The main objective is individual animal tracking: each bovine must be assigned a unique and persistent identifier to ensure reliable counting without duplicates (even in the event of crossing paths or leaving/re-entering the camera's field of view), as well as movement statistics (density heatmap, time spent in each zone). Project Scope: - Cattle detection using YOLOv8 (or equivalent SOTA model) - Multi-object tracking (ByteTrack or BoT-SORT) to assign a unique and persistent ID to each animal - Handling of occlusions and re-identification after temporary loss of tracking - Generation of a heatmap/density map based on tracked paths - Real-time counting without duplicates (target more than 15 FPS) - Web dashboard (Flask) displaying the video stream annotated with IDs, the live count, and the heatmap - Deployment on edge hardware (Raspberry Pi 4 or Jetson Nano) for 100% local processing Expected Deliverables: - Trained YOLOv8 model (.pt + .onnx) - Configured and calibrated tracking module - Documented Python script (complete pipeline: capture → detection → tracking → counting) → heatmap) - Functional Flask dashboard - Export of tracking data (CSV/JSON: positions, trajectories, counts) - Technical documentation and deployment guide - Source code on a Git repository Required skills: - Python, OpenCV - YOLOv8 / Ultralytics - Multi-object tracking algorithms (ByteTrack, BoT-SORT, DeepSORT) - Flask or equivalent lightweight web framework - Experience deploying on embedded hardware (Raspberry Pi / Jetson Nano) is a plus To apply, please provide: - Your previous projects involving object detection and/or tracking (links or examples welcome) - Your approach to preventing ID switches in a dense herd - An estimated timeframe and budget for this project