Techniques are disclosed for determining a location of an object based at least in part on a motion of the object. The techniques include generating a motion profile based at least in part on motion data received from a mobile device that is associated with the object. The techniques further include receiving, from a camera at a location, a plurality of images that identifies a candidate motion of a candidate object through at least a portion of the location. The techniques further include generating a candidate motion profile corresponding to the candidate motion of the candidate object based at least in part on the plurality of images. Based at least in part on a score generated by comparing the motion profile with the candidate motion profile, the techniques may determine that the candidate object is the object.

Techniques are disclosed for determining a location of an object based at least in part on a motion of the object. The techniques include generating a motion profile based at least in part on motion data received from a mobile device that is associated with the object. The techniques further include receiving, from a camera at a location, a plurality of images that identifies a candidate motion of a candidate object through at least a portion of the location. The techniques further include generating a candidate motion profile corresponding to the candidate motion of the candidate object based at least in part on the plurality of images. Based at least in part on a score generated by comparing the motion profile with the candidate motion profile, the techniques may determine that the candidate object is the object.

Methods and systems for mapping boundaries of a given environment by a processor of a computer system, the method comprising: determining a trajectory of the body in the given environment over the given time period; and determining, based on the trajectory of the body in the given environment, one or more of an outer boundary of the given environment, and an inner boundary of the given environment. Methods and systems for mapping functionalities of a given environment executable by a processor of a computer system, the method comprising determining a pattern of movement of a body in the given environment in a given time period; and determining a functional identity of at least one zone in the given environment based on the pattern of movement of the body to obtain a mapped given environment.

A method for classifying tracks in radar detections of a scene acquired by a stationary radar unit, comprises: acquiring radar detections of the scene using the static radar unit; feeding at least a portion of the radar detections into a tracker module for producing track-specific feature data indicating a specific track in the scene, feeding at least a portion of the radar detections into a scene model comprising information about scene-specific features aggregated over time, and information indicating areas in the scene with expected ghost target detections and areas with expected real target detections, wherein at least a subset of the scene-specific features is determined from the radar detections; classifying the specific track as belonging to a real target or to a ghost target by relating the specific track to a position in the scene model.

A method for classifying tracks in radar detections of a scene acquired by a stationary radar unit, comprises: acquiring radar detections of the scene using the static radar unit; feeding at least a portion of the radar detections into a tracker module for producing track-specific feature data indicating a specific track in the scene, feeding at least a portion of the radar detections into a scene model comprising information about scene-specific features aggregated over time, and information indicating areas in the scene with expected ghost target detections and areas with expected real target detections, wherein at least a subset of the scene-specific features is determined from the radar detections; classifying the specific track as belonging to a real target or to a ghost target by relating the specific track to a position in the scene model.

A system for automatically maintaining focus while tracking remote flying objects includes an interface and processor. The interface is configured to receive two or more images. The processor is configured to determine a bounding box for an object in the two or more images; determine an estimated position for the object in a future image; and determine an estimated focus setting and an estimated pointing direction for a lens system.

A system for automatically acquiring focus of remote flying objects (RFOs) includes an interface and processor. The interface is configured to receive a radar data and a lens temperature data. The processor is configured to determine a focal setting for a lens system based at least in part on the radar data and the lens temperature data; and provide the focal setting for the lens system.

The invention concerns a system (100) and a method for estimating the geographic position of a target (1). The method comprises the following steps: detecting a target (1); determining the characteristics of the target (1), which characteristics at least comprise a geographic position (3) and a category of the target; tracking the detected target (1) until at least one certain predetermined criteria is not fulfilled, wherein said criteria is associated to the level of certainty for determining the geographic position (3) of the target (1). The method further comprises determining a first point in time t.sub.1 when the predetermined criteria was last fulfilled, wherein, for a second point in time t.sub.2 the following step is performed: creating a pattern (2) defining at least one possible geographic position (3) of the target (1), said pattern (2) extends at least partially around the geographic position (3) of the target (1) at t.sub.1, wherein the dimension of said pattern (2) is determined based on at least one predetermined parameter.

Upon each new detection, called pivot detection, by a radar system, the method includes the steps consisting of: grouping together, with the pivot detection, grouped detections, defined as detections that belong to a sweep preceding the sweep of the pivot detection and that have a non-nil probability according to a grouping criterion; filtering the grouped detections so as to keep only detections that are kinematically strictly coherent with the pivot detection, by: initializing a histogram, each dimension of which is a temporal variation of a coordinate measured by the radar system; computing a potential value interval for each coordinate of the pivot detection and each grouped detection; computing a minimum temporal variation and a maximum temporal variation for the or each coordinate from potential value intervals of the pivot detection and each grouped detection; incrementing the set of classes of the histogram whose index along each dimension is located between the computed minimum and maximum temporal variations; and detecting a target once at least one class of the histogram reaches a predefined value.

Upon each new detection, called pivot detection, by a radar system, the method includes the steps consisting of: grouping together, with the pivot detection, grouped detections, defined as detections that belong to a sweep preceding the sweep of the pivot detection and that have a non-nil probability according to a grouping criterion; filtering the grouped detections so as to keep only detections that are kinematically strictly coherent with the pivot detection, by: initializing a histogram, each dimension of which is a temporal variation of a coordinate measured by the radar system; computing a potential value interval for each coordinate of the pivot detection and each grouped detection; computing a minimum temporal variation and a maximum temporal variation for the or each coordinate from potential value intervals of the pivot detection and each grouped detection; incrementing the set of classes of the histogram whose index along each dimension is located between the computed minimum and maximum temporal variations; and detecting a target once at least one class of the histogram reaches a predefined value.