According to one embodiment, a method, computer system, and computer program product for using mobile devices for anomaly detection in a vehicle. The present invention may include a computer receives sensor data from at least one mobile device associated with the vehicle, where the mobile device having one or more sensors. The computer analyzes data from the one or more sensors to identify an anomaly associated with the vehicle. The computer identifies a message associated with the anomaly. The computer determines an urgency value of the message based on the anomaly. The computer transfers the message with the urgency value to the vehicle and causes the vehicle to notify the message using a vehicle notification device.

A method for re-recognizing an object image is provided based on multi-feature information capture and correlation analysis weights of an input feature map by using a convolutional layer with a spatial attention mechanism and a channel attention mechanism, causing channel and spatial information to effectively combined, which not only focus on an important feature and suppress an unnecessary feature, but also improve a representation of a feature. A multi-head attention mechanism is used to process a feature after an image is divided into blocks to capture abundant feature information and determine a correlation between features to improve performance and efficiency of object image retrieval. The convolutional layer with the channel attention mechanism and the spatial attention mechanism is combined with a transformer having the multi-head attention mechanism to focus on globally important features and capture fine-grained features, thereby improving performance of re-recognition.

A Media Analytics Co-optimizer (MAC) engine that utilizes available motion and scene information to increase the activation sparsity in artificial intelligence (AI) visual media applications. In an example, the MAC engine receives video frames and associated video characteristics determined by a video decoder and reformats the video frames by applying a threshold level of motion to the video frames and zeroing out areas that fall below the threshold level of motion. In some examples, the MAC engine further receives scene information from an optical flow engine or event processing engine and reformats further based thereon. The reformatted video frames are consumed by the first stage of AI inference.

A solution for evaluating passing vehicles can include one or more imaging devices and a computing unit for processing image data acquired by the imaging device(s). The imaging devices can acquire image data that enables the computing unit to accurately identify a location of an element of the vehicle in an area that includes a region in which the passing vehicle is located and a restricted region in which no element or portion of an element of the passing vehicle should be unnecessarily located. The computing unit can classify any element at least partially located within the restricted region and initiate an action when such an element is not classified into a category of elements that is necessary to be located within the restricted region.

A solution for evaluating passing vehicles can include one or more imaging devices and a computing unit for processing image data acquired by the imaging device(s). The imaging devices can acquire image data that enables the computing unit to accurately identify a location of an element of the vehicle in an area that includes a region in which the passing vehicle is located and a restricted region in which no element or portion of an element of the passing vehicle should be unnecessarily located. The computing unit can classify any element at least partially located within the restricted region and initiate an action when such an element is not classified into a category of elements that is necessary to be located within the restricted region.

A method of detecting occupants in a vehicle includes detecting an oncoming vehicle and acquiring a plurality of images of occupants in the vehicle in response to detection of the vehicle. The method includes performing automated facial detection on the plurality of images and adding a facial image for each face detected to a gallery of facial images for the occupants of the vehicle. The method includes performing automated facial recognition on the gallery of facial images to group the facial images into groups based on which occupant is in the respective facial images, and counting the final group of unique facial images to determine how many occupants are in the vehicle.

A method of detecting occupants in a vehicle includes detecting an oncoming vehicle and acquiring a plurality of images of occupants in the vehicle in response to detection of the vehicle. The method includes performing automated facial detection on the plurality of images and adding a facial image for each face detected to a gallery of facial images for the occupants of the vehicle. The method includes performing automated facial recognition on the gallery of facial images to group the facial images into groups based on which occupant is in the respective facial images, and counting the final group of unique facial images to determine how many occupants are in the vehicle.

A traffic information processing equipment, system and method. The traffic information processing equipment includes an image recognition and decision device and a warning device. The image recognition and decision device is configured to process a received traffic route image to identify a scene, and determine whether to perform a warning operation according to the scene to obtain a determination result. The warning device is configured to generate warning information according to the determination result for sending prompt information to vehicles in a traffic route.

A traffic information processing equipment, system and method. The traffic information processing equipment includes an image recognition and decision device and a warning device. The image recognition and decision device is configured to process a received traffic route image to identify a scene, and determine whether to perform a warning operation according to the scene to obtain a determination result. The warning device is configured to generate warning information according to the determination result for sending prompt information to vehicles in a traffic route.

A method for intelligently measuring vehicle speed based on a binocular stereo vision system includes: training a Single Shot Multibox Detector neural network to obtain a license plate recognition model; calibrating the binocular stereo vision system to acquire parameters of two cameras; detecting the license plates in the captured video frames with the license plate recognition model, locating the license plate position; performing feature point extraction and stereo matching by a feature-based matching algorithm; screening and eliminating the matching point pairs, and reserving the coordinates of the matching point pair closest to the license plate center; performing stereo measurement on the screened matching point pair to get the spatial coordinates of the position; calculating and obtaining the speed of the target vehicle. The present invention is easy to install and adjust, could simultaneously recognize multiple trained features automatically, and better suit the intelligent transportation networks and IoT (Internet of Things).