Project Title: Animal Detection and Tracking from Video Feeds
Category: Image Processing
Project File: Download Project File
Noor Rahman
noor.rahman@vu.edu.pk
mahsud-cs619
Animal Detection and Tracking from Video Feeds
Project Domain
Image Processing
Introduction
Livestock monitoring is an important task in smart farming. Detecting and tracking animals in real-time video feeds can help farmers monitor herd behavior, prevent theft, and improve farm management. This project aims to develop an automated system that uses YOLO (You Only Look Once) for cow detection and DeepSORT for real-time tracking of each animal across frames. The system will provide smooth tracking outputs (tracklets) and maintain consistent IDs for cows. As an advanced extension, students may also integrate the Segment Anything Model (SAM) to segment cows at the pixel level, showing how modern AI techniques can enhance precision.
Functional Requirements
Video Input
The system shall accept video file uploads in common formats (MP4, AVI, MOV).
The system shall support real-time webcam input.
Cow Detection & Tracking
The system shall detect cows in video frames.
The system shall assign persistent unique IDs to detected cows.
The system shall maintain ID consistency when cows are temporarily occluded.
Display bounding boxes and IDs
The system shall display bounding boxes around detected cows.
The system shall display unique IDs near each detected cow.
Display of Detection and Tracking Information
The system shall display detection information, including the number of detected cows and the average confidence score, during video processing.
The system shall display tracking performance indicators, including temporary ID switch es and frame rate (FPS), during video processing.
5. User Interface
The system shall provide a user interface that enables users to upload videos or access a webcam f or efficient detection and tracking operations.
Advanced Feature (Optional)
The system shall generate pixel-level segmentation masks for detected cows.
Tools & Technologies
Programming Language: Python
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Libraries: Ultralytics YOLOv8, DeepSORT, OpenCV, NumPy, Matplotlib
Optional (Bonus): Segment Anything Model (SAM)
IDE: Spyder
Dataset: Public cow dataset (e.g., MultiCamCows2024)
Hardware:
CPU system for training small YOLO models (slow).
GPU (local or online, e.g., Google Colab/Kaggle) recommended for faster training and real-time inference.
A working system that detects and tracks multiple cows in real time.
Visualization of bounding boxes and unique IDs on each cow.
(Bonus - Optional) Pixel-level segmentation of cows using SAM.
Supervisor:
Name: Noor Rahman
Email ID: noor.rahman@vu.edu.pk
MS Teams ID: mahsud-cs619@outlook.com
No schedules available for this project.
No reviews available for this project.