The disclosure relates to a method for making sensor data more robust to adversarial perturbations, wherein sensor data are obtained from at least two sensors, wherein the sensor data obtained from the at least two sensors are replaced in each case piecewise by means of quilting, wherein the piecewise replacement is carried out in such a way that the respectively replaced sensor data from different sensors are plausible relative to one another, and wherein the sensor data replaced piecewise are output.

In accordance with an exemplary embodiment, a system is provided that includes one or more first sensors, one or more second sensors, and a processor. The one or more first sensors are of a first sensor modality configured to obtain first sensor data pertaining to an occupancy status of one or more seats of a vehicle. The one or more second sensors are of a second sensor modality, different from the first sensor modality, configured to obtain second sensor data pertaining to the occupancy status of the one or more seats of the vehicle. The processor is coupled to the one or more first sensors and the one or more second sensors, and is configured to at least facilitate determining the occupancy status of the one or more seats of the vehicle based on a fusion of the first sensor data and the second sensor data.

A device of calculating indoor/outdoor seamless positioning on the basis of a common message format according to an embodiment may include a domain provided with a sensor for detecting a moving subject and configured to generate sensing data, a positioning domain configured to receive the sensing data from the domain and perform data fusion or sensor fusion. A method of calculating indoor/outdoor seamless positioning on the basis of the common message format according to an embodiment may include entering, by a moving subject, a detection range of a sensor provided in a domain, generating, by the sensor, sensing data necessary to calculate positioning of the moving subject, transmitting the sensing data to a positioning domain that performs data fusion or sensor fusion of the sensing data, and generating, by the positioning domain, a common message format of the sensing data for seamless positioning of the moving subject.

An autonomous robotic vehicle is capable of detecting, identifying, and locating the source of gas leaks such as methane. Because of the number of operating components within the vehicle, it may also be considered a robotic system. The robotic vehicle can be remotely operated or can move autonomously within a jobsite. The vehicle selectively deploys a source detection device that precisely locates the source of a leak. The vehicle relays data to stakeholders and remains powered that enables operation of the vehicle over an extended period. Monitoring and control of the vehicle is enabled through a software interface viewable to a user on a mobile communications device or personal computer.

A method including providing a sensor device including one or several sensors. The sensor device is arranged to perform at least one high-power type measurement and at least one low-power type measurement and includes at least one image sensor arranged to depict a person by a measurement of said high-power type. Each of the low-power type measurements over time requires less electric power for operation as compared to each of the high-power type measurements. The method includes detecting a potential presence of the person using at least one of said low-power type measurements. The method includes producing, using one of the high-power type measurements, an image depicting a person and detecting a presence of the person based on im-age analysis of the image. The method includes detecting, using at least one of the low-power type measurements, a maintained presence of the person. The method includes failing to detect a maintained presence of the person.

An exemplary method includes maintaining a receiver-side mesh-vertices list, receiving duplicative-vertex information from a sender, and responsively reducing the receiver-side mesh-vertices list in accordance with the received duplicative-vertex information, and rendering, using the reduced receiver-side mesh-vertices list, viewpoint-adaptive three-dimensional (3D) personas of a subject at least in part by weighting video pixel colors from different video-camera vantage points of video cameras that capture video streams of the subject, the weighting being performed according to a respective geometric relationship of each video-camera vantage point to a user-selected viewpoint.

Methods, devices, and systems may be utilized for detecting one or more properties of a plant area and generating a map of the plant area indicating at least one property of the plant area. The system comprises an inspection system associated with a transport device, the inspection system including one or more sensors configured to generate data for a plant area including to: capture at least 3D image data and 2D image data; and generate geolocational data. The datacenter is configured to: receive the 3D image data, 2D image data, and geolocational data from the inspection system; correlate the 3D image data, 2D image data, and geolocational data; and analyze the data for the plant area. A dashboard is configured to display a map with icons corresponding to the proper geolocation and image data with the analysis.

An imaging display device includes an imaging unit, a processing unit, a display unit, and a pupil detection unit. The imaging unit includes a plurality of photoelectric conversion elements and is configured to acquire first image information. The processing unit is configured to process a signal from the imaging unit and generate second image information. The display unit is configured to display an image that is based on the signal from the processing unit. The pupil detection unit is configured to detect vector information of a pupil. The processing unit generates the second image information by processing the first image information based on the vector information on the pupil.

Systems, methods, and computer-readable for multi-spatial scale object detection include generating one or more object trackers for tracking at least one object detected from on one or more images. One or more blobs are generated for the at least one object based on tracking motion associated with the at least one object. One or more tracklets are generated for the at least one object based on associating the one or more object trackers and the one or more blobs, the one or more tracklets including one or more scales of object tracking data for the at least one object. One or more uncertainty metrics are generated using the one or more object trackers and an embedding of the one or more tracklets. A training module for detecting and tracking the at least one object using the embedding and the one or more uncertainty metrics is generated using deep learning techniques.

The present invention relates to a multi-channel lidar sensor module capable of measuring at least two target objects using one image sensor. The multi-channel lidar sensor module according to an embodiment of the present invention includes at least one pair of light emitting units configured to emit laser beams and a light receiving unit formed between the at least one pair of emitting units and configured to receive at least one pair of reflected laser beams which are emitted from the at least one pair of light emitting units and reflected by target objects.