Hrl Laboratories, LLC

A Arturo Flores

Terrell Nathan Mundhenk

Michael J Daily

Howard Neely, Iii

Shinko Yuanhsien Cheng

Terrell N Mundhenk

Howard Neely Iii Neely

Described is a training system for training a vision-based object detector. The system is configured to run an object detector on an image of a cleared scene to detect objects in the cleared scene. The object detector includes a support vector machine (SVM) or similar classifier with a feature model to generate an SVM score for object features and a spatial bias threshold to generate augmented object scores. The system designated detected objects in the cleared scene as false detections and, based on that, updates at least one of the feature model and spatial bias threshold to designate the false detections as background. The updated feature model or updated spatial bias threshold are then stored for use in object detection.

Training system for infield training of a vision-based object detector

The present invention relates to a system for detecting an object of interest in a scene. The system operates by receiving an image frame of a scene and extracting features from the image frame, the features being descriptors. The descriptors are quantized to generate PHOW features. A sliding window protocol is implemented to slide a window over the image and analyze the PHOW features that fall inside the window. Finally, the system determines if the PHOW features represent the object of interest and, if so, then designates the window as a location in the image with a detected object of interest.

System for detecting an object of interest in a scene

Described is a system for scene change detection. The system receives an input image (current frame) from a video stream. The input image is color conditioned to generate a color conditioned image. A sliding window is used to segment the input image into a plurality boxes. Descriptors are extracted from each box of the color conditioned image. Thereafter, differences in the descriptors are identified between a current frame and past frames. The differences are attenuated to generate a descriptor attenuation factor &#x3b1;. Initial scores are generated for each box based on the descriptor attenuation factor &#x3b1;. The initial scores are filtered to generate a set of conspicuity scores for each box, the set of conspicuity scores being reflective of the conspicuity of each box in the image. Finally, the conspicuity scores are presented to the user or provided to other systems for further processing.

Growing community of inventors

San Diego, CA, United States of America

A Arturo Flores