A first electronic device controls a second electronic device to measure a position of the first electronic device. The first electronic device includes a motion sensor, a network interface circuit, a processor, and a memory. The motion sensor senses motion of the first electronic device. The network interface circuit communicates with the second electronic device. The memory stores program code that is executed by the processor to perform operations that include, responsive to determining that the first electronic device has a level of motion that satisfies a defined rule, transmitting a request for the second electronic device to measure a position of the first electronic device. The position of the first electronic device is sensed and then stored in the memory. An acknowledgement is received from the second electronic device indicating that it has stored sensor data that can be used to measure the position of the first electronic device.

Method and system for building a machine learning model for finding visual targets from text queries, the method comprising the steps of receiving a set of training data comprising text attribute labelled images, wherein each image has more than one text attribute label. Receiving a first vector space comprising a mapping of words, the mapping defining relationships between words. Generating a visual feature vector space by grouping images of the set of training data having similar attribute labels. Mapping each attribute label within the training data set on to the first vector space to form a second vector space. Fusing the visual feature vector space and the second vector space to form a third vector space. Generating a similarity matching model from the third vector space.

Described herein are systems, devices, and methods for localizing and recognizing an object in an environment. In an example, a mobile cleaning robot comprises a drive system to move the mobile cleaning robot about an environment, an imaging sensor to take images of an object in the environment from different perspectives. The multiple observations include images of the object that is at least partially occluded by an obstacle. A controller circuit of the mobile robot can, for multiple different locations in a map of the environment, calculate respective fractional visibility values using the plurality of images. The fractional visibility values each represent a probability of the object being visible through the corresponding location. The controller circuit can localize and recognize the object based on the fractional visibility values at the multiple locations on the map.

A content processing method is disclosed, including: obtaining a description text of to-be-processed content and an image included in the to-be-processed content; performing feature extraction on the description text based on text semantics to obtain a text eigenvector; performing feature extraction on the image based on image semantics to obtain an image eigenvector; combining the text eigenvector with the image eigenvector to obtain an image-text multi-modal vector; and generating an estimated click-through rate of the to-be-processed content according to the image-text multi-modal vector.

Various embodiments of the disclosed subject matter provide systems, methods, architectures, mechanisms, apparatus, computer implemented method and/or frameworks configured for tracking Earth orbiting objects and adapting SSN tracking operations to improve tracking accuracy while reducing computational complexity and resource consumption associated with such tracking.

An information processing apparatus acquires a stereo image, performs matching of feature points of a first number smaller than the number of pixels in a first image to estimate three-dimensional positions of the feature points with respect to a stereo camera, sets the feature point determined to be acquired from a space set in a field of view of an imaging unit as a target point, sets surrounding points of a second number greater than the number of the feature points for which the three-dimensional positions are estimated in an image area within a predetermined distance range from the target point in the first image, and determines whether the target point is the feature point indicating a feature of an object existing in the space based on differences between the three-dimensional positions of the surrounding points and the target point with respect to the stereo camera.

Embodiments of the present disclosure disclose an anomaly detector for detecting an anomaly in a sequence of poses of a human performing an activity. The anomaly detector includes an input interface configured to accept input data indicative of a distribution of the sequence of poses, a memory configured to store a discriminative one-class classifier having a pair of complementary classifiers bounding normal distribution of pose sequences in a reproducing kernel Hilbert space (RKHS), a processor configured to embed the input data into an element of the RKHS and classify the embedded data using the discriminative one-class classifier, and an output interface configured to render a classification result.

A network device classification process, including: monitoring network traffic of networked devices in a communications network to generate device behaviour data representing network traffic behaviours of the networked devices at different time granularities; processing the device behaviour data to classify a plurality of the networked devices as IoT devices, and others of the networked devices as non-IoT devices; accessing IoT device type data representing predetermined network traffic characteristics of respective known IoT device types; processing the device behaviour data of the IoT devices and the IoT device type data to classify each of the IoT devices as being a corresponding one of the plurality of known IoT device types; and for each of the IoT devices classified as a corresponding known IoT device type, classifying the IoT device as being in a corresponding operating state based on network traffic behaviours of the IoT device at different time granularities.

A face identification method includes performing a distance detection to obtain a detected distance value; determining whether the detected distance value is smaller than a distance threshold; when the detected distance value is smaller than the distance threshold, determining a luminance corresponding to the detected distance value and emitting an infrared light according to the luminance; capturing an infrared light image and performing face identification to the infrared light image; and when the face identification is successful, performing a corresponding event. A face identification apparatus configured to perform the face identification method is further provided.

Described herein are systems, devices, and methods for localizing and recognizing an object in an environment. In an example, a mobile cleaning robot comprises a drive system to move the mobile cleaning robot about an environment, an imaging sensor to take images of an object in the environment from different perspectives. The multiple observations include images of the object that is at least partially occluded by an obstacle. A controller circuit of the mobile robot can, for multiple different locations in a map of the environment, calculate respective fractional visibility values using the plurality of images. The fractional visibility values each represent a probability of the object being visible through the corresponding location. The controller circuit can localize and recognize the object based on the fractional visibility values at the multiple locations on the map.