An optical object detection apparatus and associated methods. The apparatus may comprise a lens (e.g., fixed-focal length wide aperture lens) and an image sensor. The fixed focal length of the lens may correspond to a depth of field area in front of the lens. When an object enters the depth of field area (e.g., due to a relative motion between the object and the lens) the object representation on the image sensor plane may be in-focus. Objects outside the depth of field area may be out of focus. In-focus representations of objects may be characterized by a greater contrast parameter compared to out of focus representations. One or more images provided by the detection apparatus may be analyzed in order to determine useful information (e.g., an image contrast parameter) of a given image. Based on the image contrast meeting one or more criteria, a detection indication may be produced.

Aspects of the disclosure relate to a dynamic distance estimation output platform that utilizes improved computer vision and perspective transformation techniques to determine vehicle proximities from video footage. A computing platform may receive, from a visible light camera located in a first vehicle, a video output showing a second vehicle that is in front of the first vehicle. The computing platform may determine a longitudinal distance between the first vehicle and the second vehicle by determining an orthogonal distance between a center-of-projection corresponding to the visible light camera, and an intersection of a backside plane of the second vehicle and ground below the second vehicle. The computing platform may send, to an autonomous vehicle control system, a distance estimation output corresponding to the longitudinal distance, which may cause the autonomous vehicle control system to perform vehicle control actions.

A method determining user liveness is provided that includes calculating, by a device, eye openness measures for a frame included in captured authentication data, and storing the eye openness measures in a buffer of the device. Moreover the method includes calculating confidence scores from the eye openness measures stored in the buffer, and detecting an eye blink when a maximum confidence score is greater than a threshold score.

Techniques related to improved video coding based on face detection, region extraction, and tracking are discussed. Such techniques may include performing a facial search of a video frame to determine candidate face regions in the video frame, testing the candidate face regions based on skin tone information to determine valid and invalid face regions, rejecting invalid face regions, and encoding the video frame based on valid face regions to generate a coded bitstream.

The disclosure provides a method for calculating a passenger crowdedness degree, comprising: establishing a video data collection environment and starting collecting video data of passengers getting on and off; reading the collected video data of passengers getting on and off and pre-processing a plurality of successive image frames of the video data; identifying a human head according to the pre-processing result and taking the detected human head as a target object to be tracked by mean-shift; and judging the behaviours of getting on and off of a passenger in the area where the target object is positioned and determining the crowdedness degree of passengers inside a vehicle according to the numbers of the passengers getting on and off. The disclosure also provides a system for calculating a passenger crowdedness degree. The disclosure can effectively reduce the false detection, leak detection and error detection of the head top.

A system for predicting occurrence of a defective image includes: an input device configured to input image data into an image forming apparatus; and a hardware processor configured to analyze a spatial frequency of gradient distribution of an image in accordance with a size of a density irregularity specific to the image forming apparatus with respect to the input image data and to calculate a probability of a conspicuous density irregularity of the size in regard to the image formed by the image forming apparatus based on the image data with reference to an index of correlation between an analysis result and an evaluation value of the density irregularity.

Systems and methods for assessing text legibility in an electronic document are disclosed. According to certain aspects, the electronic document may include a text layer and a background layer, and an electronic device may generate a text mask comprising a set of glyphs at certain positions. The electronic device may analyze the text mask to generate an output comprising a set of bounding boxes that indicate legibility degrees of the respective glyphs included in the text mask. The electronic device may display the output for review and assessment by a user, who may use the electronic device to facilitate any modifications to the electronic document.

To present a determination result with respect to input data and also a reason of the determination result to a user, an extraction unit configured to extract a plurality of feature amounts from an image including an inspection target object, a determination unit configured to determine an anomaly degree of the inspection target object on the basis of the extracted feature amounts, and an image generation unit configured to generate a defect display image representing a defect included in the inspection target object on the basis of contribution degrees of the respective feature amounts with respect to the determined anomaly degree are provided.

The face recognition and identification system comprises a camcorder configured to capture facial image with and without facial accessories; an image pre-processing unit to normalize the captured facial image thereby create window and apply a Haar like features; a feature extraction unit to extract a set of features from the pre-processed facial image; a classifier to classify the set of features in groups including facial image with and without facial accessories; a control unit comprises an artificial intelligence face acknowledgment model to recognize a face and identify a person with and without facial accessories upon comparing the set of features with an image database stored in a cloud server; and a user interface to show the identified person, wherein the user interface concludes the credibility of the individual's face contingent upon the perceived face certainty level, wherein the higher the certainty level the higher the genuineness of the individual.

Implementations described herein relate to methods, systems, and computer-readable media to render a foreground video. In some implementations, a method includes receiving a plurality of video frames that include depth data and color data. The method further includes downsampling the frames of the video. The method further includes, for each frame, generating an initial segmentation mask that categorizes each pixel of the frame as foreground pixel or background pixel. The method further includes determining a trimap that classifies each pixel of the frame as known background, known foreground, or unknown. The method further includes, for each pixel that is classified as unknown, calculating and storing a weight in a weight map. The method further includes performing fine segmentation to obtain a binary mask for each frame. The method further includes upsampling the plurality of frames based on the binary mask for each frame to obtain a foreground video.