Abstract: A method for use in processing of image data stream is described. The method comprising: providing input data comprising at least one sequence of image data pieces indicative of a region of interest. Processing one or more image frames for detection of one or more foreground pixels regions. Collecting adjacent foreground pixels and determining one or more blobs associated with one or more suspected foreground objects in the region of interest Selecting one or more blobs and generating image portion indicative of one or more suspected foreground objects. And processing said one or more image portions for detection of one or more foreground objects and corresponding one or more object classes. For generating output data comprising class probability for one or more objects detected in one or more frame images of said image data stream.

Abstract: A system and method for use in data analysis are provided. The system comprises a data processing utility configured to receive and process input data, comprising: plurality of neural network modules capable for operating in a training mode and in a data processing mode in accordance with the training; a network training utility configured for operating the neural network modules in the training mode utilizing selected set of training data pieces for sequentially training of the neural network modules in a cascade order to reduce an error value with respect to the selected set of the training data pieces for each successive neural network module in the cascade; and an abnormality detection utility configured for sequentially operating said neural network modules for processing input data, and classifying said input data as abnormal upon identifying that all the neural network modules provide error values being above corresponding abnormality detection thresholds.

Abstract: A method is provided, suitable for use in extraction of foreground objects from image stream. The method comprises: providing input image data of a region of interest, providing background model of said region of interest, and utilizing said background model for processing each image of the input image data. The processing comprises: determining a background gradient map for pixels in said background model and an image gradient map for pixels in the image; defining a predetermined number of one or more segments in said image and corresponding one or more segments in the background model; determining, for each image segment, an edge density factor is a first relation between the image and background gradient maps for said segment; and calculating foreground detection threshold based on said certain relation, thereby enabling use of said foreground detection threshold for classifying each pixel in the segment as being a foreground or background pixel.

Abstract: A calibration device is presented for use in a surveillance system for event detection. The calibration device comprises an input utility for receiving data indicative of an image stream of a scene in a region of interest acquired by at least one imager and generating image data indicative thereof, and a data processor utility configured and operable for processing and analyzing said image data, and determining at least one calibration parameter including at least one of the imager related parameter and the scene related parameter.