146 results about "Object-class detection" patented technology

Described, is a system for object detection via multi-scale attentional mechanisms. The system receives a multi-band image as input. Anti-aliasing and downsampling processes are performed to reduce the size of the multi-band image. Targeted contrast enhancement is performed on the multi-band image to enhance a target color of interest. A response map for each target color of interest is generated, and each response map is independently processed to generate a saliency map. The saliency map is converted into a set of detections representing potential objects of interest, wherein each detection is associated with parameters, such as position parameters, size parameters, an orientation parameter, and a score parameter. A post-processing step is applied to filter out false alarm detections in the set of detections, resulting in a final set of detections. Finally, the final set of detections and their associated parameters representing objects of interest is output.

The present invention provides a collision avoidance apparatus and method employing stereo vision applications for adaptive vehicular control. The stereo vision applications are comprised of a road detection function and a vehicle detection and tracking function. The road detection function makes use of three-dimensional point data, computed from stereo image data, to locate the road surface ahead of a host vehicle. Information gathered by the road detection function is used to guide the vehicle detection and tracking function, which provides lead motion data to a vehicular control system of the collision avoidance apparatus. Similar to the road detection function, stereo image data is used by the vehicle detection and tracking function to determine the depth of image scene features, thereby providing a robust means for identifying potential lead vehicles in a headway direction of the host vehicle.

Disclosed are an object detection method and an object detection device. The object detection method comprises a step of obtaining plural detection results of a current frame according to plural object detection methods; a step of setting initial probabilities of the plural detection results of the current frame; a step of calculating a movement frequency distribution diagram representing movement frequencies of respective pixels in the current frame; a step of obtaining detection results of a previous frame; a step of updating the probabilities of the plural detection results of the current frame; and a step of determining a final list of detected objects based on the updated probabilities of the plural detection results of the current frame.

Techniques are disclosed for detecting foreground objects in a scene captured by a surveillance system and tracking the detected foreground objects from frame to frame in real time. A motion flow field is used to validate foreground objects(s) that are extracted from the background model of a scene. Spurious foreground objects are filtered before the detected foreground objects are provided to the tracking stage. The motion flow field is also used by the tracking stage to improve the performance of the tracking as needed for real time surveillance applications.

Methods, apparatuses, and computer-readable media are provided for maintaining blob trackers for video frames. For example, a first blob tracker maintained for a current video frame is identified. The first blob tracker is associated with a blob detected in one or more video frames. The blob includes pixels of at least a portion of a foreground object in the one or more video frames. It is determined that the first blob tracker is a first type of tracker. Trackers having the first type are associated with objects that have transitioned at least partially into a background model (referred to as sleeping objects and sleeping trackers). One or more interactions are identified between the first blob tracker and at least one other blob tracker. The at least one other blob tracker can be the first type of tracker or can be a second type of tracker that is not a sleeping tracker (the second type of tracker is not associated with an object that has transitioned at least partially into the background model. A characteristic of the first blob tracker can then be modified based on the identified one or more interactions. Modifying the characteristic of the first blob tracker can include transitioning the first blob tracker from the first type of tracker to the second type of tracker, updating an appearance model of the first blob tracker, and / or other suitable characteristic of the first blob tracker.

An apparatus, system, and method are disclosed for identifying a target object. An object detection module detects objects by matching data from one or more sensors to known data of a target object and determining one or more correlation metrics for each object. An object tracking module tracks geographic locations for detected objects over time using subsequent data from the one or more sensors. A contextual data module determines one or more contextual indicators for detected objects based on the data from the one or more sensors. An artificial intelligence module estimates probabilities that detected objects comprise the target object based on the correlation metrics, the geographic locations, the contextual indicators, and one or more target contextual indicators associated with the target object. The artificial intelligence module estimates the probabilities using an artificial intelligence model, such as a Bayesian network.

In one aspect, lines in image data of an event are automatically found and repaired. For example, the event may be a sporting event which is played on a field, and the line segment is a field line on the field which may be obscured by a player, game ball or other object. The line segment is automatically detected in a mask image, and a portion of the line segment which is occluded by the object is automatically determined, and the object is automatically removed. The line segment can also be repaired. Optionally, a virtual viewpoint of the event is provided from the image, with the line repaired and the object removed. In another aspect, an object in an image of an event is automatically located by detecting blobs in the image which meet at least one specified criterion, such as size, aspect ratio, density or color profile.