Abstract: A system for detection of a vehicle that blocks an ambulance while the ambulance is responding to an emergency call. The system includes a camera mounted on the ambulance that captures images or video of the surrounding area. A processor, such as an edge computing device with a graphical processing unit (GPU), analyzes the captured data to identify vehicles obstructing the ambulance's path. The system can identify both a leading violating vehicle that directly blocks the ambulance and a primary violating vehicle further ahead that may be causing the obstruction. The system also includes a communication unit to transmit data to a web-based portal or a cloud-based AI engine for further analysis and action.

Abstract: A method of determining a target of a subject's gaze from an image of the subject within a scene is disclosed. The method comprises analyzing the image and determining information associated with the image. A processor configured to analyze the image and determine information associated with the image is also disclosed. Finally, a non-transitory computer readable storage device comprising computer executable instructions for determining a target of a subject's gaze is disclosed.

Abstract: A method of determining a target of a subject's gaze from an image of the subject within a scene is disclosed. The method comprises analyzing the image and determining information associated with the image. A processor configured to analyze the image and determine information associated with the image is also disclosed. Finally, a non-transitory computer readable storage device comprising computer executable instructions for determining a target of a subject's gaze is disclosed.

Abstract: First, a plurality of models that predict categories of input data are pooled. At least one of the plurality of models is a model trained by machine learning. Next, each of a plurality of hybrid model candidates that judge the categories are created by selecting and combining two or more models from among the plurality of pooled models. Then, by comparing the plurality of hybrid model candidates, one of the plurality of hybrid model candidates is selected as a hybrid model.

Abstract: A tailgating detection system and method includes a single monocular camera, an embedded computer system for determining a tailgating event based on images captured by the camera, the determining of the tailgating event including detecting and tracking wheels of front and tailgating vehicle candidates and determining respective speeds relative to a speed of a vehicle that the camera is mounted and using frame rate of the camera, a location determining device for determining the location where the tailgating event occurred, and a communication unit for transmitting a notification of the tailgating event including the location where the tailgating event occurred.

Abstract: A method and system for checking data gathering conditions or image capturing conditions associated with images during AI based visual-inspection process. The method comprises generating a first representative (FR1) image for a first group of images and a second representative image (FR2) for a second group of images. A difference image data is generated between FR1 image and the FR2 image based on calculating difference between luminance values of pixels with same coordinate values. Thereafter, one or more of a plurality of white pixels or intensity-values are determined within the difference image based on acquiring difference image data formed of luminance difference-values of pixels. An index representing difference of data-capturing conditions across the FR1 image and the FR2 image is determined, said index having been determined at least based on the plurality of white pixels or intensity-values, for example, based on application of a plurality of AI or ML techniques.

Abstract: A method and system for checking data gathering conditions or image capturing conditions associated with images during AI based visual-inspection process. The method comprises generating a first representative (FR1) image for a first group of images and a second representative image (FR2) for a second group of images. A difference image data is generated between FR1 image and the FR2 image based on calculating difference between luminance values of pixels with same coordinate values. Thereafter, one or more of a plurality of white pixels or intensity-values are determined within the difference image based on acquiring difference image data formed of luminance difference-values of pixels. An index representing difference of data-capturing conditions across the FR1 image and the FR2 image is determined, said index having been determined at least based on the plurality of white pixels or intensity-values, for example, based on application of a plurality of AI or ML techniques.

Abstract: A system for gaze direction detection is disclosed herein. The system comprises a face detection unit configured to detect face information from a captured image of a spectator, a head pose unit configured to detect a head pose angle based on the face information; an eye measurement unit configured to detect eye information of at-least one of a left eye or a right eye of the spectator based on the detected face information; an eye-gaze detection unit configured to estimate a gaze angle of the at-least one of the left eye or the right eye of the subject based on the eye information; a conditional gaze detection unit configured to calculate a resultant gaze angle based on the estimated gaze angles of the left eye and right eye and the head pose angle; and an effective gaze detection unit configured to estimate an effective gaze angle based the resultant gaze angle and head-pose angle.