Project Title: Title: Human Detection and Tracking
Category: Image Processing
Project File: Download Project File
Hina Rafique
hina.rafique@vu.edu.pk
live:hina.rafique
Title: Human Detection and Tracking
Project Domain / Category
Image Processing/ Research
Abstract / Introduction
Object detection and tracking is one of the rapidly growing areas of computer vision. We can use it in different domains. In this project, we are using it to analyze the data of CCTV footage from retail stores. We will analyze this data to detect humans and they’re in ouT count. After detection we will analyze data for buyer to non buyer ratio.
Functional Requirements:
Data collection which is CCTV footage from retail stores.
The system shall optimize the video for processing by resizing frames to a standard resolution (e.g., 640x480).
The system shall perform frame enhancement if needed (e.g., brightness/contrast adjustment).
The system shall use an object detection algorithm (e.g., YOLO, SSD, or OpenCV DNN) to detect humans in each frame.
Human count: The system shall assign a unique ID to each detected person using tracking algorithms.
The system shall track movement of individuals frame-by-frame.
The system shall allow the user to define a virtual entrance/exit line within the video frame.
The system shall detect entry (IN) when a person crosses the line into the store area and exit (OUT) when the person crosses in the opposite direction.
The system shall keep track of cumulative IN and OUT counts for the selected video period.
Analysis of data on the basis of in out and buyer non-buyer ratio in graphical manner
GUI for users to interact with the system and to show graphical analysis of the system.
Non-Functional Requirements (NFRs)
Performance Requirements
The system shall process video frames at a minimum rate of 10–15 frames per second (FPS) on standard hardware for real-time detection.
Detection accuracy for human presence should be at least 85–90% under normal lighting conditions.
The system shall maintain consistent tracking even in crowded scenes with partial occlusion.
Scalability
The system shall be designed to handle multiple video sources (e.g., more than one CCTV camera) without significant performance degradation.
The architecture should support future integration of more advanced detection models or cloud-based deployment.
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The system interface shall be simple and intuitive, allowing users to easily select video files, set virtual lines, and view analytical results.
Visual results (counts, ratios, graphs) shall be displayed in a clear and understandable manner for non-technical users.
Reliability
The system shall be able to recover from temporary frame loss or video interruptions without requiring a restart.
Tracking IDs should remain consistent across frames to ensure accurate counting.
Security
Access to video data and analytical reports shall be restricted to authorized users. The system shall ensure that all stored footage and analysis results are protected from unauthorized access or modification.
Maintainability
The system code shall be modular to allow easy updates or algorithm replacements (e.g., switching from YOLOv5 to YOLOv8).
Documentation for setup, dependencies, and model training shall be provided for maintenance and further development.
Portability
The system shall be compatible with common operating systems such as Windows and Linux. The solution should be deployable on both local machines and cloud environments.
Accuracy & Robustness
The system shall maintain detection reliability under varying lighting and crowd conditions. False positive and false negative rates should be minimized through threshold tuning and model optimization.
Data Integrity
The system shall ensure that detected counts (IN/OUT) and analytical results are stored accurately without duplication or loss.
Visualization Quality
Graphical analysis (buyer-to-non-buyer ratio, hourly traffic trends) shall be dynamically updated and exportable as images or PDF reports
Tools:
OpenCV
Image AI
Tensor flow
YOLOV3, Keras
Supervisor:
Name: Hina Rafique
Email ID: hina.rafique@vu.edu.pk
MS Teams ID: hina.rafique@outlook.com
No schedules available for this project.
No reviews available for this project.