CCTV Employee Tracking

Designed an AI-powered CCTV employee tracking system using Python and Django to monitor employee movement across multiple office cameras and provide centralized workplace activity tracking. The system uses computer vision and face recognition technology to identify authorized employees from registered facial images and track their movement between different rooms, corridors, and office areas. Developed a centralized dashboard where administrators can register employees, upload reference photographs, manage camera configurations, and review employee movement history across connected CCTV cameras.

Implemented a multi-camera monitoring architecture to process video streams, detect recognized employees, record camera-specific detection events, and organize movement data by employee, location, and timestamp. Designed employee tracking workflows to help identify where an employee was detected, which office areas they visited, and the approximate timeline of their movement throughout the workplace. Integrated AI-based face detection and recognition with object tracking techniques to improve identification consistency across camera feeds and reduce repeated processing where possible.

Designed role-based access control for administrators, HR personnel, and developers. Administrators can manage users, employees, cameras, system configurations, and access permissions. HR personnel can search for employees, review authorized attendance and movement records, and generate reports. Developers can access technical monitoring tools, camera connection information, processing logs, and system diagnostics for maintenance and troubleshooting.

Planned integration with existing CCTV infrastructure, real-time video stream processing, GPU-accelerated inference, and scalable worker-based processing to support multiple cameras simultaneously. Designed data management workflows for storing employee profiles, recognition events, camera locations, timestamps, and movement histories in a structured database. Additional reporting capabilities include employee detection timelines, location-based activity history, camera-wise detection records, and searchable movement reports.

The system aims to improve workplace visibility, simplify employee movement investigations, and reduce manual video review by enabling authorized personnel to search recorded detection events through a centralized interface. The architecture can be extended with attendance device integration, configurable monitoring zones, notifications, and analytics dashboards as organizational requirements evolve.

Privacy and security considerations include restricted access to facial data and video records, secure storage, audit logging, defined retention policies, and human review of recognition results. Face recognition should be treated as a probabilistic identification method, not definitive proof of identity, and employee monitoring should follow applicable privacy laws and organizational policies.