Research On Counting Pedestrians In Video Surveillance

With the progress of science and technology, the technology of intelligent video surveillance is playing an increasingly important role in our lives day by day. Now large-scale video surveillance systems are widely used in various public places, how to analyze and deal with these massive amounts of surveillance video data to extract the useful information, which is currently a hot research topic and a difficulty in the field of intelligent video surveillance. In the practical application of intelligent monitoring, there is one of the key problems which is the counting of large-scale pedestrians, at present the counting of pedestrians is not only connected with the safety industry, but also plays an irreplaceable role in the traffic, the commerce, et al. Therefore the study of the counting of pedestrians is a subject which has an important practical and long-term significance role.This paper proposes a kind of fast and slow gaussian mixture model to detect the moving targets. In this step, this paper first use K-means method to initialize the background and the parameters of the background model, and then the background deduction method is used in the current frame for motion target detection, then a series of processes such as the neighborhood deduction, the mathematical morphology, etc, are also used to remove the false foreground pixels in the candidate region which is detected in the above steps, the goal of this process is to accurate the detected moving object regions. At last this paper uses both fast and slow updating ways to maintain the background according to the test results of the background model. In addition, there may be some shadow areas after the above steps when detect the moving targets regions, in order to accurate the statistical number of the pedestrians, the process of shadow removing is necessary, in this paper, the algorithm based on color space model is used for shadow detecting and removing.This paper uses the method which is based on the underlying characteristics to count the number of pedestrians. This paper mainly choose the pedestrians’ foreground pixels, motion vector and color characteristics to count the number. This part can be specific divided into counting the number of pedestrians under the bevel and vertical scene scenarios. In bevel scenario, this paper first uses the moving target detection method which is introduced above for extracting foreground pixel, then uses the optical flow algorithm to estimate the motion vector, and gives weights for the foreground pixels at the virtual door which is set before, finally carries on the function fitting to transform the underlying characteristics into the high-level features which can describe the pedestrians’ number. There is an advantage that donnot consider the problem of occlusion In vertical scenario, this paper Increases the extraction of the head region feature, it can be specific to extract pixels of the region of pedestrians’ hair, and then use these above steps which are introduced in bevel scenario’s to count pedestrians’ number. Experiments shows that The accuracy of this method can reach more than90%in both scenarios. So the method of counting pedestrians’ number can satisfy the need of video monitoring.Finally, this paper analyses the shortcomings which are occurred in the course of the research process, and then puts forward the work plan for the next step.