A Real Time Face Tracking System based on Multiple Information Fusion

Face tracking is one of key steps in real surveillance system. In this paper, a real-time face tracking system using multiple objects tracking algorithm is proposed. Multiple objects tracking algorithms typically consist of two parts: object detection and data association. In our system, we particularly use Multi-task convolutional neural network (MTCNN) to detect faces. Simultaneously, aiming at dealing with the tracking failure caused by object occlusion or rapid object movement, we use multiple features including appearance feature, motion feature, and shape feature for tracking. Furthermore, a judgement method is applied to measure whether tracking is successful or not. After that, depending on the tracking state, we adjust the weights of different features for feature fusion. From the experimental results, it can be concluded that, compared with the traditional tracking algorithms the proposed multi-object tracking algorithm has a better tracking effect in the real scenes.