Study Of Local Feature Description And Camera Relay On Video Surveillance Images

Anti-blur and anti-local low illumination image feature descriptors and the construction of relay adjacency topology of surveillance cameras are the application basis and research focuses in video surveillance.In video surveillance,the basic applications are target detection and target matching,which are based on feature matching.Local features can be widely used because they can largely abandon the shortcomings of the global features that are susceptible to background clutter and occlusion.Due to different time and different location,different viewing angles and different surveillance cameras,illumination changes and occlusions,the obtained images of the same target are often very different.Some local areas are less bright and some local areas are blurred.When directly using local feature descriptors such as SIFT(Scale Invariant Feature Transform)and SURF(Speeded Up Robust Features),it is impossible to extract more or higher quality feature points,which brings about difficulties to robust matching between images.The massive deployment of surveillance cameras requires the establishment of camera relay relationships more intelligent since manual management is unable to adapt to the large size of surveillance cameras.Therefore,it is of great theoretical and practical significance to research on anti-blur and anti-local low illumination image feature descriptors as well as on the construction of relay adjacency topology of surveillance camera.The research is carried out on anti-blur and anti-local low illumination image feature descriptors and the construction of relay adjacency topology of surveillance cameras.One new model and three new algorithms are proposed,and they are analyzed and verified with theoretical illustration and experimental demonstration.Firstly,in order to solve the problems of little feature points and feature points with low quality that are extracted from images with local low contrast,blurred part and unclear details in video surveillance,the Bottom-Up and Top-Down visual attention models are studied.Combining with the Bottom-Up visual attention model driven by objective content and the Top-Down visual attention model guided by subjective command,the local feature descriptor extraction model of the image is proposed.It satisfies the visual cognitive process from Bottom-Up to Top-Down in human visual system.The model obtains the total saliency map of the original image through the Bottom-Up visual attention model,and then the image local feature descriptor is extracted from the total saliency map for image matching and target recognitionby using the Top-Down visual attention model.The proposed model solves the problem of the difficulty and complexity of the current Top-Down visual attention model,and it is more suitable for extracting local feature points from blurred images,smooth images and local low-contrast images than other existing models.Secondly,in order to solve the problem of extracting too fewer feature points from fuzzy images,a new anti-blur image local feature descriptor FSIF(Fuzzy and Scale Invariant Feature Descriptor)is proposed.The descriptor takes the advantages of illumination invariance and high distinctiveness of LBP(Local Binary Pattern)and introduces the LBP descriptor into the construction process of the SIFT descriptor.The Gaussian scale pyramid of the SIFT descriptor is replaced by the local binary pattern Gaussian scale pyramid in order to extract the feature points of the FSIF.The parameters of contrast threshold and principal curvature threshold of FSIF descriptor are optimized in order to extract more accurate feature points.The descriptor has stronger robustness to the changes of illumination.It can accurately extract more feature points from the blurred target image and significantly improve the matching rate of the blurred images.Then,in order to solve the problem of extracting too fewer of feature points and feature points with low quality from local low-contrast images,an ALLI(Anti-Local Low Illumination Feature Descriptor)is proposed.The ALLI descriptor takes the advantages that the image phase information is local contrast invariant and it can include the angles,lines,texture and contour,structure in the image.First,the phase consistency image of the original image is extracted,and then the feature points and feature descriptors are extracted from the phase consistency image by using the similar method with SURF algorithm.The Euclidean distance between two feature points is calculated to perform the initial matching,and then the MSAC(M-estimator SAmple Consensus)is used to eliminate the mismatched feature point pairs to achieve accurate matching.The ALLI algorithm is more suitable for matching local low-contrast images than the existing descriptors,and it can improve the matching and recognition between local low-contrast images.Finally,being aimed at the automation issue of video camera relay scheduling in large scale video camera surveillance network,a video surveillance camera adjacency relation algorithm is proposed by using GIS(Geographic Information System)and GPS(Global Positioning System).When a new camera is to be deployed in a section of a road,the related camerasdeployedoriginally in the same section are searched by using the spatial index of GIS based on its GPS information,and then the adjacency relationship topology between the new camera and the related ones is generated by using the sorting algorithm according to their GPS information.The algorithm is simple in operation and it is easy to be implemented in the real world.Specifically,it can be used in the construction of camera relay-surveillance topology,such as automatic real-time tracking on abnormal behavior and the analysis of the escape routes in city streets and other traffic roads.It can improve the current working mode that relies on humans to observe and monitor images during and afterwards,which is challenging in workload and low in efficiency.It provides a technical basis for massive deployment of cameras and real-time relay response of monitoring systems,with great economic and social benefits.