基于在线特征选择的实时多姿态人脸跟踪

针对复杂条件下的人脸跟踪问题, 将显著区域跟踪算法和基于 Adaboost 的人脸检测算法相结合, 研发了一个实时多姿态人脸跟踪系统. 系统采用数据关联结果, 自动选择和切换检测器与跟踪器, 并通过引入环境信息增强跟踪算法的稳定性. 实验表明, 系统可在目标姿态变化、摄像机运动等复杂条件下进行自动人脸检测与跟踪, 对 320x240 的图像序列处理速度达到 10-12帧/秒.

Online Adaptive Fast Multipose Face Tracking Based on Visual Cue Selection

This paper presents a system that is able to reliably track multiple faces under varying poses (tilted and rotated) in real time. The system consists of two interactive modules. The first module performs the detection of the face that is subject to rotation. The second module carries out online learning-based face tracking. A mechanism that switches between the two modules is embedded into the system to automatically decide the best strategy for reliable tracking. The mechanism enables a smooth transit between the detection and tracking modules when one of them gives either nil or unreliable results. Extensive experiments demonstrate that the system can reliably carry out real time tracking of multiple faces in a complex background under different conditions such as out-of-plane rotation, tilting, fast nonlinear motion, partial occlusion, large scale changes, and camera motion. Moreover, it runs at a high speed of 10~12 frames per second (fps) for an image of 320~240.