Camera Abnormal Movement and Foreign Object Invasion Detection Based on Cumulative Edge Distribution Probability Model In practice, there often exist some occasions where video surveillance can only be realized by using rechargeable batteries due to the high cost of power supply. In order to extend the battery life, images can only be taken at a certain interval. Obviously, image difference is an important method to obtain various changes in this case. Among these changes, foreign invasion is one of the focuses. However, due to bolt shedding, abnormal weather and other factors, the camera may have abnormal movement which may seriously affect the detection effect of image difference method. Therefore, it is an urgent issue to find an effective way to detect the abnormal movement of camera and improve the detection accuracy of foreign invasion. Considering the characteristics of this kind of image sequences, we propose an effective algorithm to detect the camera abnormal movement and foreign object invasion based on a cumulative edge distribution probability model. Since sky region is relatively simple, we only discuss changes in sky region to detect camera abnormal movement. Our algorithm has 6 basic steps: firstly, segment the sky region; secondly, extract the edge information of the current image and the preceding adjacent image in the image sequence; thirdly, determine if the edge information of two adjacent images coincide. If consistent, then go to the next step, otherwise, it indicates that the camera has abnormal movement, then alarm; fourthly, calculate the cumulative edge probability distribution model in the sky region by using the historical image sequence; fifthly, by using adaptive Parzen window, determine if foreign object invasion exists by comparing the probability model of cumulative edge distribution with edge distribution of the current image; sixthly, update the cumulative edge distribution probability model in sky region. The algorithm achieves good results in practical applications. Through the test of thousands of images taken in the wild, the detection accuracy of camera abnormal movement and foreign object invasion reaches 95%.

Hysteresisbased selective gaussianmixture model for realtime background update We propose a novel Mixture of Gaussian (MOG)based realtime background update technique. The proposed technique consists of a new selective matching scheme based on the combined approaches of component ordering and winnertakesall. This matching scheme not only selects the most probable component for the first matching with new pixel data, greatly improving performance, but also simplifies pixel classification and component replacement in case of no match. Further performance improvement achieved by using a new simple and functional component variance adaptation formula. Also in this technique, the proposed new hysteresisbased component matching and temporal motion history schemes improve segmentation quality. Component hysteresis matching improves detected foreground object blobs by reducing the amount of cracks and added shadows, while motion history preserves the integrity of moving objects boundaries, both with minimum computational overhead. The proposed background update technique implicitly handles both gradual illumination change and temporal clutter problems. The problem of shadows and ghosts is partially addressed by the proposed hysteresisbased matching scheme. The problem of persistent sudden illumination changes and camera movements are addressed at frame level depending on the percentage of pixels classified as foreground. We implemented three different stateoftheart background update techniques and compared their segmentation quality and computational performance with those of the proposed technique. Experimental results on reference outdoor sequences and real traffic surveillance streams show that the proposed technique improved segmentation accuracy for extracting moving objects of interest compared to other reference techniques.