A surveillance information generation apparatus (2000) includes a first surveillance image acquisition unit (2020), a second surveillance image acquisition unit (2040), and a generation unit (2060). The first surveillance image acquisition unit (2020) acquires a first surveillance image (12) generated by a fixed camera (10). The second surveillance image acquisition unit (2040) acquires a second surveillance image (22) generated by a moving camera (20). The generation unit (2060) generates surveillance information (30) relating to object surveillance, using the first surveillance image (12) and first surveillance information (14).

Processes, systems, and devices for occlusion detection for video-based object tracking (VBOT) are described herein. Embodiments process video frames to compute histogram data and depth level data for the object to detect a subset of video frames for occlusion events and generate output data that identifies each video frame of the subset of video frames for the occlusion events. Threshold measurement values are used to attempt to reduce or eliminate false positives to increase processing efficiency.

Monitoring operations of a polishing system includes obtaining a time-based sequence of reference images of a component of the polishing system performing operations during a test operation of the polishing system, receiving from a camera a time-based sequence of monitoring images of an equivalent component of an equivalent polishing system performing operations during polishing of a substrate, determining a difference value for the time-based sequence of monitoring images by comparing the time-based sequence of reference images to the time-based sequence of monitoring image using an image processing algorithm, determining whether the difference value exceeds a threshold, and in response to determining the difference value exceeds the threshold, indicating an excursion.

Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for automated camera positioning for feeding behavior monitoring. In some implementations, a system obtains an image of a scene, a spatial model that corresponds to a subfeeder, and calibration parameters of a camera, the system determines a size of the subfeeder in the image of the scene, the system selects an updated position of the camera relative to the subfeeder, the system provides the updated position of the camera relative to the subfeeder to a winch controller, and the system moves the camera to the updated position.

A visible light sensor may be configured to sense environmental characteristics of a space using an image of the space. The visible light sensor may be controlled in one or more modes, including a daylight glare sensor mode, a daylighting sensor mode, a color sensor mode, and/or an occupancy/vacancy sensor mode. In the daylight glare sensor mode, the visible light sensor may be configured to decrease or eliminate glare within a space. In the daylighting sensor mode and the color sensor mode, the visible light sensor may be configured to provide a preferred amount of light and color temperature, respectively, within the space. In the occupancy/vacancy sensor mode, the visible light sensor may be configured to detect an occupancy/vacancy condition within the space and adjust one or more control devices according to the occupation or vacancy of the space. The visible light sensor may be configured to protect the privacy of users within the space via software, a removable module, and/or a special sensor.

A method for receiving a plurality of captures of a vehicle from a plurality of locations. Calculating a plurality of signatures of the vehicle, each respectfully associated with a corresponding capture from the plurality of captures. Associating the plurality of signatures resulting in a single signature and utilizing the single signature for identifying the vehicle.

Embodiments relate to systems, methods and computer readable media for gaming monitoring. In particular, embodiments process images to determine presence of a gaming object on a gaming table in the images. Embodiments estimate postures of one or more players in the images and based on the estimated postures determine a target player associated with the gaming object among the one or more players.

A system and method for detecting movement of a non-light-of-sight (NLOS) object in a space outside a line-of-sight (LOS) of a camera is disclosed. The camera acquires a sequence of successive images that each include a set of pixels, at least a subset thereof representing a target, being at least part of a visible object located within the LOS of the camera and impacted by light scattered from the NLOS object. The set of pixels of two images of the sequence, acquired from different positions of the camera, are registered into a common coordinate system, giving rise to two registered images. A target light intensity value is calculated for both registered images based on at least part of the set of pixels representing the target in the respective registered image. Movement of the NLOS object is detected based on a variation in the target light intensity value between registered images.

A throwing position acquisition method and apparatus, a computer device, and a storage medium. The method includes: acquiring image frames of a target video; acquiring a projectile position in each image frame; acquiring a trajectory starting point position of the target object based on the projectile position in each image frame; acquiring, based on projectile positions corresponding to at least one group of image frames in the image frames, a first height value corresponding to a case that the target object is thrown; and acquiring a throwing position of the target object based on the first height value and the trajectory starting point position of the target object.

A target object detection apparatus (20) includes an image generation unit (220) that generates, from three-dimensional information acquired by processing a reflection wave of an electromagnetic wave irradiated toward an inspection target, a two-dimensional image of the inspection target viewed from a predetermined direction; an area detection unit (230) that detects, from the two-dimensional image, each of at least two detection areas of detection target objects recognized by using at least two recognition means; and an identification unit (240) that identifies the detection target object, based on a positional relationship between the detected at least two detection areas.