Abstract: The invention provides methods and systems for reliably detecting objects in a received video stream from a camera. Objects are selected and a bound around selected objects is calculated and displayed. Bounded objects can be tracked. Bounding is performed by using Histogram of Oriented Gradients and linear Support Vector Machine classifiers.

Abstract: A method for automatic spatial calibration of a network of cameras along a road includes, processing a frame that is obtained from each camera of the network to automatically identify an image of a pattern of road markings that have a known spatial relationship to one another. The identified images are used to calculate a position of each camera relative to the pattern of road markings that is imaged by that camera. Geographical information is applied to calculate an absolute position of a field of view of each camera.

Abstract: A method for automatically matching video streams from two cameras of a camera network includes obtaining a video stream of frames that are acquired by each of the cameras. Each video stream includes images of moving objects. A time signature for each of the video streams is calculated. Each time signature is indicative of a time at which an image of one the objects is located at a predetermined part of the frame. A temporal offset of one of the signatures relative to the other signature is calculated such that, when applied to one of the signatures, a correspondence between the signatures is maximized. The temporal offset is applicable to video streams that are acquired by the two cameras to determine if a moving object that is imaged by one of the cameras is identical to a moving object that is imaged by the other camera.

Abstract: In queues, persons—or objects (120) in general—move inside an area (110) to a target (112), such as to a counter. The queue has movement characteristics in terms of speed, waiting times and queue form. A computer-implemented approach obtains the characteristics by receiving a sequence (140) of image frames (141, 142, 143, 49) that represent the surveillance area (110); calculating flow vectors that indicate an optical displacement for the sequence (140) of image frames (141/142, 142/143); extending one of the flow vectors as lead vector in extension directions; determining intermediate vectors by using flow vectors along the extension directions; and selecting one the intermediate vectors as the new lead vector. The steps are repeated to concatenate the lead vectors to the movement characteristics (190).