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This research aims to develop an automated video analytics framework for effective staff monitoring using multi-camera systems.

June 21, 2026Open Access

A Multi-Camera Staff Monitoring Framework Using Pose-Based Tracking, Temporal Activity Classification, and Homography-Driven Cross-View Re-Identification

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Randomized trial evaluates automated monitoring of staff in retail environments, suggesting improvements for surveillance systems.

This research aims to develop an automated video analytics framework for effective staff monitoring using multi-camera systems.
Utilized a modular video analytics framework incorporating human detection, activity classification, and identity resolution.
Implemented a dual-lane architecture separating tracking from biometric processing.
Evaluated the system's performance on the EPFL CVLAB dataset with four synchronized cameras.
Reported qualitative tracking results highlighting performance under varying crowd densities.
Identified challenges in face recognition accuracy under CCTV conditions.
Noted a domain gap caused by the use of synthetically generated training data.

Alagoni et al. (2026) studied this question.

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