Towards a Practical PTZ Face Detection and Tracking System

Abstract

We address the problem of automatic face detection and tracking in uncontrolled scenarios using a pan-tilt-zoom (PTZ) network camera, which could prove most helpful in forensic applications. The detected faces are associated with the corresponding people and trajectories. The dynamic nature of real-world scenarios and real-time restrictions complicate our task. Different from previous work which use a mixture of wide angle cameras and PTZ cameras, we explore the limits to what can be expected from a single PTZ camera. The system first detects and tracks pedestrians in zoomed-out mode, then selects, using a scheduler, a person to zoom in to. After zoom in, we come back to wide area mode, and solve the person-to-person, face-to-person and face-to-face data association problems. Extensive experiments in challenging indoor and outdoor uncontrolled conditions demonstrate the effectiveness of the proposed system.