Research Of Several Key Techniques In Intelligent Video Surveillance System

In recent years, public security situation has become increasingly complex and serious, making the scale of video monitoring increased, and the monitoring mode develop from the traditional video monitoring to intelligent video surveillance. However, intelligent video surveillance technology is a multi-disciplinary crossover and combination involved with computer vision, pattern recognition, artificial intelligence, data mining, and so on, the facing problems and practical scene are complex, and it is still at the stage of exploration and development.In actual intelligent video surveillance system, complex environment such as bad weather(haze,rain,snow), lack light of dark night, uneven illumination, and so on, causing the loss of image quality, which bring the innate difficulties to the subsequent video analysis; complex background such as dynamic background changes, illumination changes, camera shakes, making background modeling and foreground detection difficult; in the face of huge amounts of video image data, it is necessary to retrieve or query related target information accurately, making the demand of robust target feature extraction and matching technology become increasingly urgent; long-term stably tracking of video moving target in the circumstances of scaling, occlusion, object resembles the background, is also an urgent issue to tackle. For the above mentioned problems, the current intelligent video surveillance and related technology is still immature, easily lead to adverse consequences of misrepresentation and omission, which restricted the performance of the intelligent video surveillance system in practical applications. This article focuses research on several key techniques in the intelligent video surveillance system, such as image enhancement, complex background modeling and foreground object detection, feature extraction and matching of target, target tracking. Main work is summarized as follows:1. A multi-scale image enhancement algorithm is proposed which combining visual properties and general log-radio model. The algorithm uses global adaptive adjustment of the human visual properties and takes a similar logarithmic transformation to the global image brightness; using local linear relationship between the human subjective feelings and the actual light intensity and the sensitive characteristics of human visual, combines with four direction Sobel gradient image, to adjust local contrast of the image; using the adaptive different scales of guide filter function and the generalized log-ratio model, integrating effective information of different scales images to get the final multi-scale enhancement image. The experimental results show that the proposed algorithm has realized the image contrast enhanced and the effective dynamic range compressed, strengthened and kept the details of the image texture and edge, with a stronger anti-noise ability, effectively solved the problem of enhancement with low illumination image and infrared image in video monitoring system.2. According to Irradiation_Reflection model of Retinex an image enhancement algorithm is proposed. Using the edge-preserving and adaptive guide filter function as the surround function to estimate the different scales irradiation images which react the whole structure of image; using the bounded generalized log-ratio model instead of traditional operation, removing illuminate components from the original image to segment the different scales of the reflection image; fusing the effective information of the different scales reflection images and getting the final multi-scale reflection enhanced image which react the nature of the object. The experimental results show that the proposed algorithm overcomes the emergence of halo effect of the multisacle Retinex and computing overflow effectively. The effect of the algorithm is particularly obvious for night images and haze images.3. In the respect of complex background modeling and foreground object detection: 1) in order to detect the change of movement state of target, propose an efficient method for foreground objects detection based on dual background models, realizing the detection of abandoned and moved objects in the field of view; 2) on the basis of the analysis and summary of the features and disadvantages of Vi Be algorithm, a kind of complex background model and foreground detection method is proposed. The algorithm gives comprehensive consideration to the same location pixels of the relevance of time and the correlation of space with its adjacent pixels, which can significantly improve the adaptability and robustness of the background model such as dynamic backgrounds, illumination changes and camera shakes, achieving the goal of accurate detection of foreground.4. A fast matching algorithm is proposed based on corner detection and Gray-value Differential Invariants(GDI) local feature descriptor. Establish less layer pyramid and use Shi-Tomasi algorithm to extract a reasonable number of strong corners for the layers. Using low-level gray scale differential invariants with geometric meaning to establish a local feature descriptor based on region histogram, realizing the stability of the corner matching. Experimental results demonstrate that the proposed algorithm has the invariance for rotation, scaling, illumination changes, smaller viewpoint changes, blur and so on, the algorithm has better matching precision and real time.5. A Mean Shift target tracking algorithm is proposed based on the fusion of color features and texture features. Improves the Epanechnikov kernel function model of the Mean Shift algorithm, reduces the amount of calculation and highlight the target gray level information inside the tracking window; considering the adaptability of the scale with target tracking. When the scale of target changes, the target and background are similar, can also be a good real-time tracking for moving targets.