Abstract: A system and method for authoring and implementing context-aware applications (CAPs) are disclosed. The system and method enables users to record their daily activities and then build and deploy customized CAPs onto augmented reality platforms in which automated actions are performed in response to user-defined human actions. The system and method utilizes an integrated augmented reality platform composed of multiple camera systems, which allows for non-intrusive recording of end-users' activities and context detection while authoring and implementing CAPs. The system and method provides an augmented reality authoring interface for browsing, selecting, and editing recorded activities, and creating flexible CAPs through spatial interaction and visual programming.

Abstract: A joint training network including a multi-heard module comprising a network input, a feature network coupled to the network input and including a feature detector decoder outputting interest points and a descriptor generator decoder outputting descriptors, the feature detector decoder and the descriptor generator decoder coupled in parallel, a depth network including a monocular depth prediction decoder and outputting a depth map, a flow network including an image segmentation decoder and outputting a segmented image, a segmentation network including a warping module outputting a rotation and translation and an input warp signal to a segmentation decoder outputting a residual flow and a pose network including a fully connected pose estimator coupled to an adder that receives input from the pose estimator and the residual flow from the segmentation decoder, the adder outputting an optical flow.

Abstract: A control system for positioning a marker over a pre-existing roadway surface mark. The control system has one or more imagers having a field of view for imaging an area of roadway mark surface encompassing the roadway mark and a computer having a machine learning network to process the roadway mark image and position the marker over the pre-existing roadway mark.

Abstract: An image recognition method, electronic device, and storage medium are provided and relate to the fields of artificial intelligence, computer vision, deep learning, image processing, and the like. The method includes: performing joint training on a first sub-network configured for recognition processing and a second sub-network configured for retrieval processing in a classification network by adopting an identical set of training data to obtain a trained target classification network, wherein, the first sub-network and the second sub-network are twin networks that are consistent in network structures and share a set of weights; and inputting image data to be recognized into the target classification network to obtain a recognition result. By adopting the method, the accuracy of the image recognition may be improved.