A Video Surveillance System On Face Recognition And Motion Detection

This thesis was finished on the basis of a video surveillance system, which includes face recognition and motion detection algorithm and is designed to search and recognize faces appearing in a video sequence and detect motion in selected region.By using skin-color feature, especial location and pixel features of eyes in face area, an efficient face detection algorithm was designed. After face detection, Discrete Cosine Transform (DCT) was used to extract a set of observation, which is provided to train and recognize faces in the way of Hidden Markov Model (HMM). In order to solve the shortcoming that traditional motion detection algorithm can not be used to detect slow moving objects from an image sequence, an improved method was proposed by rebuilding the background. It was coded in the system and users can decide which motion detection method detects slow or fast target. Several modules were added to make the system perfect, including video camera module, alarm module and records reviewing module, etc. On the platform of the completed system, the effects of face detection and recognition have been displayed. Comparison between traditional algorithm of frame difference and improved one of background difference has been tested. The result showed advantage of the improved one, which can be applied in detecting slow moving objects.The face detection and recognition algorithm used in this thesis is real-time and precise, can search and recognize faces quickly.The improved method of background difference is obviously better than the traditional one when slow targets are moving. The two methods can be used in different condition.In the summary, the completed system with detailed structure and function is suitable to be used to real-time video surveillance.