Abstract: A tracking system for tracking objects within a field of view is disclosed. The field of view may include a first zone and an adjacent zone of interest where at least two gates are associated with respective sides of the first zone within the field of view. The first camera is configured to detect when an object crosses one of the at least two gates and track the object throughout the first zone and the zone of interest. The tracking system is configured to generate a first event message in response to the object being tracked from one of the gates into the zone of interest and subsequently leaving the first zone through a dedicated gate of the at least two gates.

Abstract: A method for machine-based training of a computer-implemented network for common detecting, tracking, and classifying of at least one object in a video image sequence having a plurality of successive individual images. A combined error may be determined during the training, which error results from the errors of the determining of the class identification vector, determining of the at least one identification vector, the determining of the specific bounding box regression, and the determining of the inter-frame regression.

Abstract: An image signal indicative of the head pose of a person in a scene, as provided by a camera may be processed by estimating, as a function of the image signal received by the image sensor, the head pose of the person. A view frustum of the person may be generated from the estimated head pose. A visual frustum of attention or VFOA of the person may be generated from the view frustum. From the visual frustum, a predicted movement trajectory of the person or an estimated scene attention map as a function of the visual frustum of attention, preferably by identifying in the attention map at least one object to be controlled may be generated.

Abstract: A tracking system for tracking objects within a field of view is disclosed. The field of view may include a first zone and an adjacent zone of interest where at least two gates are associated with respective sides of the first zone within the field of view. The first camera is configured to detect when an object crosses one of the at least two gates and track the object throughout the first zone and the zone of interest. The tracking system is configured to generate a first event message in response to the object being tracked from one of the gates into the zone of interest and subsequently leaving the first zone through a dedicated gate of the at least two gates.

Abstract: A method of measuring illumination of an environment may include capturing (e.g. via an RGB-D camera arrangement) images of an illuminated environment. The method may further include extracting from the image captured light emittance values as well as albedo values of surface areas (“patches”) across the environment. The method also includes computing illumination values of surface areas across the environment as a function of the emittance values and the albedo values.

Abstract: The presence of a stationary object is to be reliably recognized. Thereto, a presence detection device for detecting a presence of an object in its environment is provided, which comprises a movement detection unit for detecting an initial movement of the object in the environment of the presence detection device and for outputting a movement signal depending on the detection as well as a control unit for generating an activation signal depending on the movement signal. Moreover, the presence detection device comprises a camera, which can be activated by the activation signal, for obtaining a video signal of the environment of the presence detection device and an evaluation unit for generating a presence signal relating to the presence of the object by evaluating the video signal.

Abstract: A lighting system including a set of lighting devices for lighting an environment that may be controlled by a method. The method may include receiving, from one or more image sensors (e.g. a RGB-D camera—W), an image signal including a sequence of images of the environment under different conditions of illumination and light reflection. The method may further include processing the image signal to provide a light source identification signal representative of light sources affecting the environment, and controlling the lighting devices as a function of the light source identification signal, and, possibly, of human occupancy and activity.

Abstract: A method for surface reconstruction may include illuminating at least one object simultaneously by light emitted by a plurality of luminaires spaced apart from another. The method may further include recording a photographic sequence comprising a plurality of individual images of the object(s). The method may further include reconstructing at least one visible object surface of the object by photometric stereo analysis.

Abstract: A lighting system including a set of lighting devices for lighting an environment that may be controlled by a method. The method may include receiving, from one or more image sensors (e.g. a RGB-D camera—W), an image signal including a sequence of images of the environment under different conditions of illumination and light reflection. The method may further include processing the image signal to provide a light source identification signal representative of light sources affecting the environment, and controlling the lighting devices as a function of the light source identification signal, and, possibly, of human occupancy and activity.

Abstract: An image signal indicative of the head pose of a person in a scene, as provided by a camera may be processed by estimating, as a function of the image signal received by the image sensor, the head pose of the person. A view frustum of the person may be generated from the estimated head pose. A visual frustum of attention or VFOA of the person may be generated from the view frustum. From the visual frustum, a predicted movement trajectory of the person or an estimated scene attention map as a function of the visual frustum of attention, preferably by identifying in the attention map at least one object to be controlled may be generated.

Abstract: The presence of a stationary object is to be reliably recognized. Thereto, a presence detection device for detecting a presence of an object in its environment is provided, which comprises a movement detection unit for detecting an initial movement of the object in the environment of the presence detection device and for outputting a movement signal depending on the detection as well as a control unit for generating an activation signal depending on the movement signal. Moreover, the presence detection device comprises a camera, which can be activated by the activation signal, for obtaining a video signal of the environment of the presence detection device and an evaluation unit for generating a presence signal relating to the presence of the object by evaluating the video signal.

Abstract: A method for recognizing the presence of at least one person in a specified spatial region by way of at least one camera is provided. The method may include recording a sequence of images of the spatial region by way of the at least one camera, performing a person recognition on the basis of at least one image of the image sequence, and then classifying or remaining classified a person object recognized by way of the person recognition as a real person if it is possible to assign to the person object a movement that has been recognized by an image evaluation of a plurality of images of the image sequence.

Abstract: A training method for object recognition, the training method comprising: providing at least one top-view training image; aligning a training object present in the training image along a pre-set direction; labelling at least one training object from the at least one training image using a pre-defined labelling scheme; extracting at least one feature vector for describing the content of the at least one labelled training object and at least one feature vector for describing at least one background scene; and training a classifier model based on the extracted feature vectors.

Abstract: A trajectory estimation apparatus includes: an image acceptance unit which accepts images that are temporally sequential and included in the video; a hierarchical subregion generating unit which generates subregions at hierarchical levels by performing hierarchical segmentation on each of the images accepted by the image acceptance unit such that, among subregions belonging to hierarchical levels different from each other, a spatially larger subregion includes spatially smaller subregions; and a representative trajectory estimation unit which estimates, as a representative trajectory, a trajectory, in the video, of a subregion included in a certain image, by searching for a subregion that is most similar to the subregion included in the certain image, across hierarchical levels in an image different from the certain image.

Abstract: A trajectory estimation apparatus includes: an image acceptance unit which accepts images that are temporally sequential and included in the video; a hierarchical subregion generating unit which generates subregions at hierarchical levels by performing hierarchical segmentation on each of the images accepted by the image acceptance unit such that, among subregions belonging to hierarchical levels different from each other, a spatially larger subregion includes spatially smaller subregions; and a representative trajectory estimation unit which estimates, as a representative trajectory, a trajectory, in the video, of a subregion included in a certain image, by searching for a subregion that is most similar to the subregion included in the certain image, across hierarchical levels in an image different from the certain image.