Abstract: Individual identification and tracking are provided via combined video and LiDAR systems. In various embodiments, a virtual plane may be generated. A video frame including the virtual plane is recorded via a first imaging modality. One or more objects are detected in the video frame when the one or more objects enters the virtual plane. An identifier is assigned to each of the one or more objects. One or more three-dimensional shapes are detected at the second imaging modality when the one or more three-dimensional shapes enters the virtual plane. For each of the one or more objects, a corresponding shape of the one or more three-dimensional shapes is determined. Each identifier is assigned to the respective corresponding shape of the one or more three-dimensional shapes. After assigning each identifier, a plurality of positional data is recorded for each of the one or more three-dimensional shapes.

Abstract: Individual identification and tracking are provided via combined video and LiDAR systems. In various embodiments, a virtual plane may be generated. A video frame including the virtual plane is recorded via a first imaging modality. One or more objects are detected in the video frame when the one or more objects enters the virtual plane. An identifier is assigned to each of the one or more objects. One or more three-dimensional shapes are detected at the second imaging modality when the one or more three-dimensional shapes enters the virtual plane. For each of the one or more objects, a corresponding shape of the one or more three-dimensional shapes is determined. Each identifier is assigned to the respective corresponding shape of the one or more three-dimensional shapes. After assigning each identifier, a plurality of positional data is recorded for each of the one or more three-dimensional shapes.

Abstract: Mobile LiDAR platforms for vehicle tracking are provided. In various embodiments, a time-series of point clouds is received from a LiDAR sensor. Each point cloud of the time series of point clouds is projected onto a plane. The projected point clouds are provided to a convolutional neural network. At least one bounding box of a vehicle within each of the projected point clouds is received from the convolutional neural network. A relative speed of the vehicle is determined from the bounding boxes with the projected point clouds. A reference speed of the LiDAR sensor is determined from the GPS receiver. From the relative speed and the reference speed, an absolute speed of the vehicle is determined.

Abstract: Distracted driver detection is provided. In various embodiments, a video frame is captured. The video frame is provided to a trained classifier. The presence of a predetermined action by a motor vehicle operator depicted therein is determined from the trained classifier. An alert is sent via a network indicating the presence of the predetermined action and at least one identifier associated with the motor vehicle operator.