Face Detection And Tracking Based On Near-infrared Technology

In this paper, we study the human face detection and motion tracking technology under the condition of near infrared video sequence. The focus of this paper is to reduce the effects of illumination change as a starting point, with near infrared illumination invariant of facial video as the research object, the research of face detection and tracking algorithm is proposed. This algorithm puts forward a robust and accurate research methods, to adapt the demand of complex environment and it improve the performance of face detection tracking system.Paper’s main work is as follows:1) Reviewing the research background and significance of current near-infrared video surveillance technology, and then analysis the research status of face detection and tracking algorithm.2) We focus on active near infrared technology. When light is poor, compared with visible light and thermal infrared imaging technology, then analysis the advantage of near-infrared video surveillance technology.3) The traditional algorithm has some flaws: computational complexity、lower sensitivity、poor anti-jamming capability and so on. Resulting in that it is not suitable for practical application. Due to the tracking technology can not automatically select a tracking area. On this basis, to put forward a fast and efficient algorithm of the face detection, let it can widely use in practice.4) It is simply to introducing Mean Shift tracking technology, according to its principle, then analysis the limitation of Mean Shift Algorithm complex scenes. For example, when the target is moving too fast or the occlusion is happening, the effect of Tracking sharply decline, tracking easily fail. Finally, went to verify this by experimental data.5) Improve the efficiency of tracking. Put forward the Mean Shift algorithm fusion Kalman filter video tracking method. We adopt to improve the characteristics of the track, overcome the deficiency of the single tracking algorithm. By comparing the experimental, have verified that the proposed improvement program can effectively improve the tracking Quality of Mean Shift algorithm in the complex scenes.