Systems and methods for demographic determination using image recognition. The method includes analyzing an image with a pre-trained lightweight neural network model, where the lightweight neural network model generates a confidence value, and comparing the confidence value to a threshold value to determine if the pre-trained lightweight neural network model is sufficiently accurate. The method further includes analyzing the image with a pre-trained heavyweight neural network model for the confidence value below the threshold value, wherein the pre-trained heavyweight neural network model has above about one million trainable parameters and the pre-trained lightweight neural network model has a number of trainable parameters below one tenth the heavyweight model, and displaying demographic data to a user on a user interface, wherein the user modifies store inventory based on the demographic data.

In some examples, one or more processors may receive at least one first visible light image and a first thermal image. Further, the processor(s) may generate, from the at least one first visible light image, an edge image that identifies edge regions in the at least one first visible light image. At least one of a lane marker or road edge region may be determined based at least in part on information from the edge image. In addition, one or more first regions of interest in the first thermal image may be determined based on at least one of the lane marker or the road edge region. Furthermore, a gain of a thermal sensor may be adjusted based on the one or more first regions of interest in the first thermal image.

The present invention relates to a method for acquiring an object information, the method comprising: obtaining an input image acquired by capturing a sea; obtaining a noise level of the input image; when the noise level indicates a noise lower than a predetermined level, acquiring an object information related to an obstacle included in the input image from the input image by using a first artificial neural network, and when the noise level indicates a noise higher than the predetermined level, obtaining a noise-reduced image of which the environmental noise is reduced from the input image by using a second artificial neural network, and acquiring an object information related to an obstacle included in the sea from the noise-reduced image by using the first artificial neural network.

Novel tools and techniques are provided for implementing digital microscopy imaging using deep learning-based segmentation and/or implementing instance segmentation based on partial annotations. In various embodiments, a computing system might receive first and second images, the first image comprising a field of view of a biological sample, while the second image comprises labeling of objects of interest in the biological sample. The computing system might encode, using an encoder, the second image to generate third and fourth encoded images (different from each other) that comprise proximity scores or maps. The computing system might train an AI system to predict objects of interest based at least in part on the third and fourth encoded images. The computing system might generate (using regression) and decode (using a decoder) two or more images based on a new image of a biological sample to predict labeling of objects in the new image.

Systems and methods of video data extraction and processing from vehicles are described. The video data is captured using a video capture device at a vehicle. Sensor data is captured using one or more vehicle sensors at the vehicle. A data message is sent from the vehicle to a vehicle management server, the data message allowing the vehicle management server to access the video data and the sensor data. One or more model outputs are generated by providing the video data to one or more machine-learning models at the vehicle management server. An event record associated with an event is constructed based on the one or more model outputs using a vehicle rules engine. A vehicle management message is generated based on the event record and is sent to the vehicle.

A method according to the present disclosure includes (a) generating N pieces of input data from one target object, (b) inputting the input data to a machine learning model and obtaining M classification output values, one determination class, and a feature spectrum, (c) obtaining a similarity degree between a known feature spectrum group and the feature spectrum for the input data, and obtaining a reliability degree with respect to the determination class as a function of the reliability degree, and (d) executing a vote for the determination class, based on the reliability degree with respect to the determination class, and determining a class determination result of the target object, based on a result of the vote.

A quantum computing based video alert system converts captured video and audio signals, in real time, into a sequence of video qubits and a sequence of audio qubits. An entanglement score is generated based on a comparison of the video qubits to historical video qubits that are verified to show malicious activity. A second entanglement score is generated based on a comparison of the audio qubits to historical audio qubits that are verified to show malicious activity. A probability score is generated for each segment of the video qubit sequence and for each segment of the audio qubit sequence. If the probability score for the video qubit sequence, the audio qubit sequence, or a combination of probability scores for both the video qubit sequence and the audio qubit sequence meet a threshold, then an alert is generated to identify possible malicious activity at the location of a CCTV camera capturing the real-time data.

A method, apparatus and system for human skeleton pose estimation includes synchronously capturing images of a human moving through an area from a plurality of different points of view, for each of the plurality of captured images, determining a bounding box that bounds the human in the captured image and identifying pixel locations of the bounding box in the image, for each of the plurality of captured images, determining 2D and single-view 3D skeletons from the pixel locations of the bounding box, determining a first, multi-view 3D skeleton using a combination of the 2D and single-view 3D skeletons, and optimizing the first, multi-view 3D skeleton to determine a final 3D skeleton pose for the human. The method, apparatus and system can further include illuminating the area with structured light during the capturing of the images of the human moving through the area.

Aspects of the invention relate to methods of authenticating a user and user authentication systems. The method comprises classifying an image of the user as authentic or non-authentic by: identifying a separation vector between a user image characteristic vector and a hyperplane generated by a machine learning algorithm; comparing the separation vector with a threshold value; and associating the user image with a classification value if the separation vector exceeds the threshold value. The user may be authenticated based on a classification decision informed by the classification value associated with the user image.

An image parsing method includes obtaining feature information of an initial image, parsing first feature information in the feature information using a first channel to obtain a first prediction result, parsing second feature information in the feature information using a second channel to obtain a second prediction result, where a size of the first feature information meets a first size range, a size of the second feature information meets a second size range, and the first size range is less than the second size range, and outputting the first prediction result and the second prediction result as a parsing result of the initial image.