A computing system may include a fail-safe learning engine configured to access camera data captured by multiple cameras positioned within an environment during a learning phase, generate training data based on the camera data captured by the multiple cameras, and construct a human detection model using the training data. The computing system may also include a fail-safe trigger engine configured to access camera data captured by the multiple cameras positioned within the environment during an active phase, and the camera data captured during the active phase may include a target object. The fail-trigger engine may further be configured to provide, as an input to the human detection model, the camera data that includes the target object and execute a fail-safe action in the environment responsive to the determination, provided by the human detection model, indicating that the target object is a human.

A computing system may include a fail-safe learning engine configured to access camera data captured by multiple cameras positioned within an environment during a learning phase, generate training data based on the camera data captured by the multiple cameras, and construct a human detection model using the training data. The computing system may also include a fail-safe trigger engine configured to access camera data captured by the multiple cameras positioned within the environment during an active phase, and the camera data captured during the active phase may include a target object. The fail-trigger engine may further be configured to provide, as an input to the human detection model, the camera data that includes the target object and execute a fail-safe action in the environment responsive to the determination, provided by the human detection model, indicating that the target object is a human.

The subject disclosure relates to techniques for detecting an object. A process of the disclosed technology can include steps for receiving three-dimensional (3D) Light Detection and Ranging (LiDAR) data of the object at a first time, generating a first point cloud based on the 3D LiDAR data at the first time, receiving 3D LiDAR data of the object at a second time, generating a second point cloud based on the 3D LiDAR data at the second time, aggregating the first point cloud and the second point cloud to form an aggregated point cloud, and placing a bounding box around the aggregated point cloud. Systems and machine-readable media are also provided.

Systems and methods for identifying writing systems utilized in documents. An example method comprises: receiving a document image; splitting the document image into a plurality of image fragments; generating, by a neural network processing the plurality of image fragments, a plurality of probability vectors, wherein each probability vector of the plurality of probability vectors is produced by processing a corresponding image fragments and contains a plurality of numeric elements, and wherein each numeric element of the plurality of numeric elements reflects a probability of the image fragment containing a text associated with a respective writing system; computing an aggregated probability vector by aggregating the plurality of probability vectors, wherein each numeric element of the aggregated probability vector reflects a probability of the image containing a text associated with a writing system that is identified by an index of the numeric element within the aggregated probability vector; and responsive to determining that a maximum numeric element of the aggregated probability vector exceeds a predefined threshold value, concluding that the document image contains one or more symbols associated with a respective writing system.

Systems and methods for identifying writing systems utilized in documents. An example method comprises: receiving a document image; splitting the document image into a plurality of image fragments; generating, by a neural network processing the plurality of image fragments, a plurality of probability vectors, wherein each probability vector of the plurality of probability vectors is produced by processing a corresponding image fragments and contains a plurality of numeric elements, and wherein each numeric element of the plurality of numeric elements reflects a probability of the image fragment containing a text associated with a respective writing system; computing an aggregated probability vector by aggregating the plurality of probability vectors, wherein each numeric element of the aggregated probability vector reflects a probability of the image containing a text associated with a writing system that is identified by an index of the numeric element within the aggregated probability vector; and responsive to determining that a maximum numeric element of the aggregated probability vector exceeds a predefined threshold value, concluding that the document image contains one or more symbols associated with a respective writing system.

The present technology provides systems, methods and computer program instructions implementing machine learning techniques to enable program processes to learn more effective feedback mechanisms to achieve desired results (e.g., reduce errors, improve form, duration, speed, and so forth) of motions and poses comprising tasks being taught or guided. In implementations an automated technology for automated creation of movement assessments from labeled video and continually learning audio, video or other feedback for use with machine learning techniques enable program processes to learn more effective feedback mechanisms to achieve desired results (e.g., reduce errors, improve form, duration, speed, and so forth) of motions and poses comprising tasks being taught or guided.

The present technology provides systems, methods and computer program instructions implementing machine learning techniques to enable program processes to learn more effective feedback mechanisms to achieve desired results (e.g., reduce errors, improve form, duration, speed, and so forth) of motions and poses comprising tasks being taught or guided. In implementations an automated technology for automated creation of movement assessments from labeled video and continually learning audio, video or other feedback for use with machine learning techniques enable program processes to learn more effective feedback mechanisms to achieve desired results (e.g., reduce errors, improve form, duration, speed, and so forth) of motions and poses comprising tasks being taught or guided.

A system and method for model-based camera tracking and image occlusion removal for a camera viewing a sports field (or other scene) includes receiving a synthesized data set comprising at least one empty field image of the field, the empty field image with at least one occlusion graphic, and camera parameters corresponding to the empty field image, training a neural network model to estimate the empty field image and the corresponding camera parameters by providing the model with an input training image comprising the empty field image with occlusion graphic, and providing the model with model output targets comprising the empty field image and the corresponding camera parameters as targets for the model, receiving by the neural network model, alive input image comprising a view of the field with live occlusions, and providing by the neural network model, using trained model parameters, estimated live camera parameters or an estimated empty field image associated with the live input image.

A system and method for model-based camera tracking and image occlusion removal for a camera viewing a sports field (or other scene) includes receiving a synthesized data set comprising at least one empty field image of the field, the empty field image with at least one occlusion graphic, and camera parameters corresponding to the empty field image, training a neural network model to estimate the empty field image and the corresponding camera parameters by providing the model with an input training image comprising the empty field image with occlusion graphic, and providing the model with model output targets comprising the empty field image and the corresponding camera parameters as targets for the model, receiving by the neural network model, alive input image comprising a view of the field with live occlusions, and providing by the neural network model, using trained model parameters, estimated live camera parameters or an estimated empty field image associated with the live input image.

A hybrid body temperature measurement system and a hybrid body temperature measurement method are provided. In the method, position sensing data is obtained. The position sensing data includes an azimuth of one or more to-be-detected objects relative to a reference position. The position sensing data is mapped to a thermal image so as to generate a mapping result. The thermal image is formed in response to a temperature. A position of the to-be-detected object in the thermal image is determined according to the mapping result. Accordingly, the detection accuracy is improved.