Multi-Camera Person Tracking

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Multi-Camera Person Tracking

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₹4,999.00

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₹4,999.00

Overview
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Detail Description

1. Abstract

Multi-Camera Person Tracking is a computer vision project designed to detect and track people across multiple camera feeds. The system uses deep learning-based person detection, object tracking, and visual feature matching to associate observations of the same person across different camera views. It can support crowd monitoring, facility management, and security operations by providing a consolidated view of movement across monitored areas. The system can display tracking results, camera information, and movement timelines through a centralized dashboard.

2. Objectives

Detect people in multiple camera feeds.
Track individuals within individual camera views.
Associate possible matches across different cameras.
Display person movement and camera-wise tracking information.
Maintain timestamps and tracking event logs.
Provide a centralized interface for monitoring multiple cameras.

3. Existing System

Traditional surveillance systems typically display camera feeds separately and depend on operators to follow people manually. Tracking an individual across several cameras can be difficult, especially in large facilities with overlapping camera views or changing lighting conditions.

3.1 Disadvantages

Requires considerable manual observation.
Following people across multiple camera feeds is challenging.
Separate camera systems may not provide a unified view.
Changes in lighting, viewing angle, and appearance can affect matching.
Occlusion and crowded scenes can interrupt tracking.

4. Proposed System

The proposed system integrates multiple camera feeds with person detection, within-camera tracking, and cross-camera feature matching. Each detected person receives a temporary tracking identifier, and the system attempts to associate observations across cameras using visual features and available timing or location information. A centralized dashboard displays camera feeds, tracking events, timestamps, and possible cross-camera matches.

5. Implementation Procedure

Connect two or more webcams, CCTV feeds, or sample video files.
Process video frames using Python and OpenCV.
Detect people using a suitable object detection model, such as YOLO.
Track detected people within each camera using an algorithm such as ByteTrack or Deep SORT.
Extract visual appearance features using a suitable re-identification model.
Compare features from different cameras to estimate possible person matches.
Use camera layout and timestamps, where available, to improve matching.
Display camera feeds, tracking identifiers, and possible matches on a dashboard.
Store tracking events and timestamps for evaluation.
Test the system with different camera angles, lighting conditions, and crowded scenes.

6. Software Requirements

Programming Language: Python
Computer Vision Library: OpenCV
Object Detection: YOLO or another compatible model
Tracking Algorithms: ByteTrack or Deep SORT
Person Re-Identification: A compatible feature-embedding model
Deep Learning Framework: PyTorch or TensorFlow
Data Processing: NumPy and Pandas
Development Environment: VS Code, PyCharm, or Jupyter Notebook
Optional Web Interface: Flask or Streamlit

7. Hardware Requirements

Processor: Intel Core i5 or equivalent recommended
RAM: Minimum 8 GB; 16 GB recommended for multiple feeds
Storage: SSD with sufficient space for video and tracking logs
Cameras: Two or more webcams or compatible CCTV cameras
GPU: Recommended for real-time deep-learning inference
Display: Monitor capable of displaying multiple camera feeds
Network: Required when connecting IP cameras or remote video streams

8. Advantages of the Project

Combines multiple camera feeds into a unified monitoring system.
Supports automated person detection and tracking.
Helps visualize movement across monitored locations.
Reduces the need for operators to follow every feed manually.
Maintains timestamped tracking events for analysis.
Can be extended for crowd analytics and facility monitoring.

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