Abstract: The present disclosure relates to a system (300) for providing route guidance using augmented reality, the system includes a processor (502) to obtain a set of orientation parameters of a subject (304) based on one or more sensors accommodated in a computing device (302) associated with the subject and determine one or more motion parameters associated with the subject to reach the destination from a target location. The processor generates a set of arrows indicating at least one direction of the path from the target location of the subject to the destination, wherein when the set of orientation parameters deviates from the one or more motion parameters, the processor configured to calculate a degree of rotation to align the set of arrows indicating the at least one direction of the path along the desired direction to reach the destination to facilitate route guidance.

Abstract: A method for object tracking includes receiving a target image of an object of interest. Latent space features of the target image is modified at a forward pass for a neural network by dropping at least one channel of the latent space features, dropping a channel corresponding to a slice of the latent space features, or dropping one or more features of the latent space features. At the forward pass, a location of the object of interest in a search image is predicted based on the modified latent space features. The location of the object of interest is identified by aggregating predicted locations from the forward pass.

Abstract: A health monitoring system for a crane (10) includes a wheel assembly (50, 70) having a wheel (120) with an axle (160) defining an axis (172) of rotation of the wheel. The health monitoring system further includes a plurality of strain gauges (208) coupled to the axle at circumferential locations around the axis of rotation. The strain gauges continuously detect strains experienced at the wheel. The health monitoring system further includes a data acquisition system (200) coupled to the wheel that receives data from the strain gauges corresponding to detected strains. The health monitoring system further includes a main controller (204) coupled to the data acquisition system.

Abstract: A computer-implemented method for tracking with visual object constraints includes receiving a lingual constraint and a video. A word embedding is generated based on the lingual constraint. A set of features is extracted for one or more frames of the video. The word embedding is cross-correlated to the set of features for the one or more frames of the video. A prediction indicating whether the lingual constraint is in the one or more frames of the video is generated based on the cross-correlation.

Abstract: A method for object tracking includes receiving a target image of an object of interest. Latent space features of the target image is modified at a forward pass for a neural network by dropping at least one channel of the latent space features, dropping a channel corresponding to a slice of the latent space features, or dropping one or more features of the latent space features. At the forward pass, a location of the object of interest in a search image is predicted based on the modified latent space features. The location of the object of interest is identified by aggregating predicted locations from the forward pass.

Abstract: A health monitoring system for a crane (10) includes a wheel assembly (50, 70) having a wheel (120) with an axle (160) defining an axis (172) of rotation of the wheel. The health monitoring system further includes a plurality of strain gauges (208) coupled to the axle at circumferential locations around the axis of rotation. The strain gauges continuously detect strains experienced at the wheel. The health monitoring system further includes a data acquisition system (200) coupled to the wheel that receives data from the strain gauges corresponding to detected strains. The health monitoring system further includes a main controller (204) coupled to the data acquisition system.

Abstract: A computer-implemented method for tracking with visual object constraints includes receiving a lingual constraint and a video. A word embedding is generated based on the lingual constraint. A set of features is extracted for one or more frames of the video. The word embedding is cross-correlated to the set of features for the one or more frames of the video. A prediction indicating whether the lingual constraint is in the one or more frames of the video is generated based on the cross-correlation.