Research On Human Object Detection And Behavior Recognition In Video Stream

Object detection, tracking and recognition based on video stream are hot issues in the field of computer vision, pattern recognition, intelligent video surveillance, senior human-computer interaction, mobile robot localization and navigation, virtual reality, which have broad application prospects.In the decades’unremitting efforts of scholars, these technologies have already more research achievements. Due to the complexity of the environment and the diversity of the goal itself in the visual system, the technologies of object detection, tracking and recognition have brought great difficulties. The practical experience shows that the technologies of object detection, tracking and recognition are far from mature in the general sense and there are still certain gaps away from practical application. So they also need to develop more practical and robust algorithms. In both the theoretical and the practical perspective, this paper studies on some correlative key issues of moving object recognition with the input video. The issues mainly focus on moving object detection, moving object tracking, motion feature representation and recognition.In the paper, the methods of background modeling are studied and an algorithm of object detection of video stream is proposed based on the pixels’ statistics classification. The pixel values of the image are seen as the combination of the foreground Gaussian distribution and the background Gaussian distribution, and the background estimation and the adaptive background update will be put up. The statistical number of the foreground pixels of the current frame determines whether the light has a larger change, and the algorithm needs to combine with the frame-difference method to detect moving object. Simulation results show that the algorithm can quickly and accurately detect the foreground object with greater adaptability. The experiment of face detection under complex background shows that the algorithm has a certain practical worth in the face detection based on skin color information. Circle detection algorithm is proposed in this paper. It firstly uses mathematical morphology method to denoising, filling and contour extraction for the binary image, and then calculates circularity index using chain code method. Experimental results show that the circle detection algorithm is simple and effective and the accuracy is less than one pixel.To solve multi-target tracking problems, this paper proposes a multi-target tracking algorithm based on a combination of corner feature. It extracts stable and symmetrical feature points of moving object using the improved Harris operator, and completes the tracking of video moving multi-target by feature matching and matching optimization. Tracking experiments show that the algorithm can complete stable matching under the changes of angle view, rotation, affine transformation, illumination and other circumstances, and can achieve stable tracking under a small partial shelter state. The classic tracking algorithm of Mean shift is not valid to fast moving object, and has also the problem of error accumulation. So this paper proposes an algorithm based on centroid weighted Kalman filter for object tracking. The algorithm firstly uses background subtraction method to lock dynamic target tracking area, and then uses the Kalman filter to predict the target’s position at the beginning of the target tracking, and then optimizes the predictive state value adopting centroid weighted method, finally updates the observation data according to the corrected state value. Simulation results show that the algorithm can detect effectively moving objects and at the same time it can quickly and accurately track moving objects with good robustness.To solve face recognition problems in a complex and changing light, the paper proposes an algorithm of face recognition based on LBP operator and EMD. Firstly, it uses a series of simple and efficient image preprocessing for improving the robustness of the algorithm, and then the LBP histogram of the image is obtained by extracting the local LBP feature. The use of EMD can complete measuring the similarity of the images by calculating the LBP histogram. The experimental results in the GTAV standard face database show that the algorithm improves significantly the recognition rate. Recognition and understanding of human behavior are the higher level of visual tasks. On the basis of various algorithms of human behavior recognition, the paper proposes an algorithm of human behavior recognition based on space-time interest point. It firstly uses3D Harris corner to extract the spatial-temporal features of different behavior, and then classifies these motion features and reduces their dimensions using K-means clustering combining with LLE method in the data space. In the process of training recognition, the geometric characteristics method of the mean Hausdorff distance completes similarity registration between image sequences. The experiments on the KTH database show that the algorithm is effective and feasible, and the stream-based trajectory method improves further the recognition accuracy.