Abstract: According to one aspect of the presently disclosed subject there is provided a computerized method for scoring performance of a move of a trainee in a trainee frame input in relation to a move of a trainer in a trainer video wherein the trainer move includes at least one trainer keyframe. The method comprising obtaining a trainee frame input comprising a trainee move, the trainee move comprising a plurality of trainee frames, based on the at least one trainer keyframe, processing the plurality of trainee frames to provide a move performance score, indicative of the performance of the trainee move in relation to the trainer move, with respect to at least one aspect of the performance. In some examples, the aspects can be one of a similarity aspect, a timing aspect, or a motion dynamics aspect or a combination thereof.

Abstract: According to one aspect of the presently disclosed subject there is provided a computerized method for scoring performance of a move of a trainee in a trainee frame input in relation to a move of a trainer in a trainer video wherein the trainer move includes at least one trainer keyframe. The method comprising obtaining a trainee frame input comprising a trainee move, the trainee move comprising a plurality of trainee frames, based on the at least one trainer keyframe, processing the plurality of trainee frames to provide a move performance score, indicative of the performance of the trainee move in relation to the trainer move, with respect to at least one aspect of the performance. In some examples, the aspects can be one of a similarity aspect, a timing aspect, or a motion dynamics aspect or a combination thereof.

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. A global optimization is applied to adjust the absolute position of the field of view of each camera of the camera network relative to an absolute position of the fields of view of other cameras of the camera network.

Abstract: An object-recognition method and system employing Bayesian fusion algorithm to reiteratively improve probability of correspondence between captured object images and database object images by fusing probability data associated with each of plurality of object image captures.

Abstract: The invention provides methods and systems for identifying objects transitioning boundaries defined around geographic areas using sensors. Objects that are vehicles are detected by license plate reader sensors. Boundaries defining geographic zones and sub-zones are pre-defined graphically by reference to a map or computed based on locations of sensors located within the geographic region. When sensor coverage is incomplete, soft zones are computed and defined. Soft zones are computed using probabilistic models, and are developed to minimize the number of escape paths from any sub-zone around sensor locations. Using these probabilistic models, additional sensors can be optimally placed for further enhancement of the detection system.

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 system and method to perform multisensory data fusion in a distributed sensor environment for object identification classification. Embodiments of the invention are sensor-agnostic and capable handling a large number of sensors of different types via a gateway which transmits sensor measurements to a fusion engine according to predefined rules. A relation exploiter allows combining sensor measurements with information on object relationships from a knowledge base. Also included in the knowledge base is a travel model for objects, along with a graph generator to enable forecasting of object locations for further correlation of sensor data in object identification. Multiple task managers allow multiple fusion tasks to be performed in parallel for flexibility and scalability of the system.

Abstract: An object-recognition method and system employing Bayesian fusion algorithm to reiteratively improve probability of correspondence between captured object images and database object images by fusing probability data associated with each of plurality of object image captures.

Abstract: A computer implemented method for selecting at least one sensor from a plurality of sensors (130, 132, 134), wherein the at least one sensor is adapted to record sensor-data that is related to a geographical area (110, 112), wherein the sensor-data comprises information about objects (537) movable between locations (A, B, C, D, E, F) of that geographical area (110, 112), wherein a data structure (120, 121, 122, 123) with a pre-processed graph represents the geographical area (110, 112), the pre-processed graph having elements comprising nodes and edges, wherein nodes (a, b, c, d, e, f) represent the locations (A, B, C, D, E, F) of the geographical area (110, 112), wherein edges represent transition times (140) for the objects (537) if moving between the locations (A, B, C, D, E, F), and wherein the pre-processed graph has a simplified number of nodes (a, b, c, d, e, f) according to rules applied to the elements, the method comprising: receiving an event (135) indicator in relation to a particular location (B)