Systems and methods for monitoring a throughway using a radio frequency identification (RFID) detection system. The RFID detection system includes (i) an image sensor configured to have a field of view directed towards a lane of the throughway; (ii) an RFID transceiver arrangement configured to interrogate RFID tags disposed on vehicles within the lane of the throughway; and (iv) a controller operatively connected to the image sensor and the RFID transceiver arrangement. The controller is configured to (1) cause the image sensor to capture a frame of image data representative of the lane of the throughway; (2) analyze the frame of image data to detect a presence of a vehicle in the lane of the throughway; (3) based on the analysis, determine a position of the vehicle relative to the RFID transceiver arrangement; and (4) configure an antenna array to generate a beam directed at the position of the vehicle.

In some embodiments, apparatuses and methods are provided herein useful to train a machine learning algorithm to detect text of interest. In some embodiments, there is provided a system to detect vertically oriented text of interest including a first data set comprising a plurality of captured digital images each depicting an object of interest and a second data set comprising a plurality of augmented digital images each depicting a captured digital image augmented with a synthetic text image; a first control circuit configured to cause the machine learning algorithm to output a machine learning model trained to automatically detect occurrences of vertically oriented text of interest based on the first data set and the second data set; at least one camera; and a second control circuit configured to execute the machine learning model to automatically detect vertically oriented text of interest on the object of interest.

Systems and methods of unsafe driving detection are provided which share partial unsafe driving behavior analyses with others in order to ensure that unsafe driving behavior is detected as early as possible. For example, in response to an event which interrupts a first detecting vehicle from collecting additional driving behavior data associated with a subject vehicle, the first detecting vehicle may transfer driving behavior data it has already collected and processed, to another detecting entity (e.g., a second detecting vehicle) in observable range of the subject vehicle.

A system for inspecting the reliability of an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: obtaining a plurality of images; categorizing the images into a plurality of image classes; calculating a plurality of probability outcomes; determining whether highest predicted probabilities of the images are less than a first threshold and whether an entropy of a predicted density of the probability outcomes exceeds a second threshold; indicating whether the image is associated with the image classes; ranking, the image amongst the plurality of images; filtering, a plurality of low reliability images according to a third threshold; providing, a likelihood of whether a user scanned a vehicle object associated with the image; and identifying a percentage of user scan failures.

An information processing apparatus that analyzes video data obtained from an image capture apparatus to detect an article that has been pre-registered is disclosed. The apparatus, in a case where an article that is determined to be a property of an article holder that has been pre-registered is detected, searches for an advertisement viewer located within a range of a predetermined distance from the article holder from among advertisement viewers that have registered the article as an article they are interested in. The apparatus then informs an advertisement viewer found by the search of location information of the article.

A system and methods are provided for determining a vehicle action during a phantom projection attack, including processing a received image to identify a traffic object, and creating from the received image multiple processed images that are applied to respective neural network (NN) models. Latent representations of the multiple processed images from each of the NN models are then fed to a combiner model trained to determine whether the latent representations indicate a phantom projection attack, and, responsively to a determination of a phantom projection attack, issuing a phantom projection indicator.

Aspects of the present disclosure relate to a method of identifying a vehicle, and a system thereof. The method can include receiving a first image of a vehicle from a first camera and classifying the vehicle in the first image with a vehicle class label. The method can also include determining a first vehicle fingerprint for the vehicle. The method can also include detecting any changes in the first vehicle fingerprint and the vehicle class label after a first time period. The detected changes in the first vehicle fingerprint can correspond to a disassembly state of the vehicle. The method can also include performing, if the vehicle class label is unchanged, at least one action in response to detected changes in the first vehicle fingerprint.

According to various embodiments, a wrinkle detection service providing server for providing a wrinkle detection method based on an artificial intelligence may include a data pre-processor for obtaining a skin image of a user from a skin measurement device and performing pre-processing based on feature points based on the skin image; a wrinkle detector for inputting the skin image pre-processed through the data pre-processing into an artificial neural network and generating a wrinkle probability map corresponding to the skin image; a data post-processor for post-processing the generated wrinkle probability map; and a wrinkle visualization service providing unit for superimposing the post-processed wrinkle probability map on the skin image and providing a wrinkle visualization image to a user terminal of the user.

An apparatus includes at least one camera configured to capture a series of image frames for traffic lanes in front of an ego vehicle, where each of the series of image frames is captured at a different one of a plurality of times. A target object detection and tracking controller is configured to process each of the image frames using pixel measurements extracted from the respective image frame to determine, from the pixel measurements, a predicted time to line crossing for a target vehicle detected in the respective image frame at a time corresponding to capture of the respective image frame.

In a computer-implemented method and associated tangible non-transitory computer-readable medium, an image of a damaged vehicle may be analyzed to generate a repair estimate. A dataset populated with digital images of damaged vehicles and associated claim data may be used to train a deep learning neural network to learn damaged vehicle image characteristics that are predictive of claim data characteristics, and a predictive similarity model may be generated. Using the predictive similarity model, one or more similarity scores may be generated for a digital image of a newly damaged vehicle, indicating its similarity to one or more digital images of damaged vehicles with known damage level, repair time, and/or repair cost. A repair estimate may be generated for the newly damaged vehicle based on the claim data associated with images that are most similar to the image of the newly damaged vehicle.