People Counting Based on Head Detection and Reidentification in Overlapping Cameras System People counting is one of the key tasks in video surveillance system. Usually, one camera cannot cover all the area for a big room or plaza, so multiple cameras will be used. For overlapping cameras system, the overlapped area should be marked for people counting. To get the area, an easy way is to calibrate the cameras. But in real practical system there are hundreds of cameras, itxe2x80x99s impracticable to calibrate all the cameras. A practicable way is to get the overlapping area is to applying image mosaic algorithm. Using local features such as SIFT cannot to find the overlapping area due to corresponding low quality and repeated similarity (such as seats and tables) in most surveillance environment. In this paper, we are committed to finding repetitive people to improve the accuracy of the population statistics. First, a person head detector is trained to detect the human head in the frame taken by each camera video and then cut it, and the images of the head pair taken by each camera are taken as a database which belongs to this camera. Then, we select one database as Gallery, image from another database as the Probe, we use a Siamese networks to match a probe with Gallery, repeating the above process until all the iterations of the probe in the database are completed, so that we find all the recurring people between the two cameras. Finally, the median of the total number of people in all video frame image counts is calculated, and the median result is taken as the final statistical result of the scene.

Object tracking using multiple camera video streams Two synchronized cameras are utilized to obtain independent video streams to detect moving objects from two different viewing angles. The video frames are directly correlated in time. Moving objects in image frames from the two cameras are identified and tagged for tracking. One advantage of such a system involves overcoming effects of occlusions that could result in an object in partial or full view in one camera, when the same object is fully visible in another camera. Object registration is achieved by determining the location of common features in the moving object across simultaneous frames. Perspective differences are adjusted. Combining information from images from multiple cameras increases robustness of the tracking process. Motion tracking is achieved by determining anomalies caused by the objectsu0027 movement across frames in time in each and the combined video information. The path of each object is determined heuristically. Accuracy of detection is dependent on the speed of the object as well as variations in direction of motion. Fast cameras increase accuracy but limit the speed and complexity of the algorithm. Such an imaging system has applications in traffic analysis, surveillance and security, as well as object modeling from multiview images. The system can easily be expanded by increasing the number of cameras such that there is an overlap between the scenes from at least two cameras in proximity. An object can then be tracked long distances or across multiple cameras continuously, applicable, for example, in wireless sensor networks for surveillance or navigation.