RealTime MultiTarget MultiCamera Tracking with SpatialTemporal Information Video security monitoring has always been an important mission for safety reason. In an entire surveillance system, there are usually several cameras distributed sparsely to cover a wide range of public areas (e.g., school, shopping mall or infrastructure). Tracking person through this cameras network is challenging due to different camera perspectives, illumination changes and pose variations. Several algorithms for MultiTarget MultiCamera tracking (MTMCT) have been proposed in offline method which has delay in getting result. Addressing the need for realtime computation of people tracks through multi camera, the paper proposes an online tracking algorithm. The contributions include (1) online realtime framework which can be used in practical application, (2) extend a single camera multi object tracking (MOT) algorithm to be suitable for multicamera tracking and (3) use spatialtemporal information to strengthen cross camera person recall performance. The proposed algorithm has been benchmarked against the literature review of MTMC algorithms.

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.