A method and device for controlling brake system based on preceding vehicle recognition are disclosed. The device comprising: a data collecting unit configured to collect front data and rear data of a preceding vehicle, a driving trajectory extracting unit configured to extract a driving trajectory of a lane in which the vehicle is traveling, a preceding vehicle analysis unit configured to determine driving information of the preceding vehicle, a preceding vehicle determining unit configured to determine intervention of the preceding vehicle or deviation of the preceding vehicle with respect to the driving trajectory, and a control unit configured to control the brake system.

Embodiments are directed to parameter adjustment for sensors. A calibration model and a calibration profile for a sensor may be provided. Calibration parameters associated with the sensor may be determined based on the calibration profile. The sensor may be configured to use a value of the calibration parameter based on the calibration profile. Trajectories may be generated based on a stream of events from the sensor. Metrics associated with the sensor events or the trajectories may be determined. If a metric value may be outside of a control range, further actions may be iteratively performed, including: modifying the value of the calibration parameter based on the calibration model; configuring the sensor to use the modified value of the calibration parameter; redetermining the metrics based on additional trajectories; if the metric is within a control range, the iteration may be terminated and the calibration profile may be updated.

Systems and methods for video presentation and analytics for live sporting events are disclosed. At least two cameras are used for tracking objects during a live sporting event and generate video feeds to a server processor. The server processor is operable to match the video feeds and create a 3D model of the world based on the video feeds from the at least two cameras. 2D graphics are created from different perspectives based on the 3D model. Statistical data and analytical data related to object movement are produced and displayed on the 2D graphics. The present invention also provides a standard file format for object movement in space over a timeline across multiple sports.

The embodiment of the present invention discloses an anti-drowning safety alarm method and device for swimming pools. Acquiring images taken by multiple cameras; inputting the images into a target detection model to detect human bodies and heads, so as to obtain human body target detection boxes and human head target detection boxes; calculating the relationship between the human heads and the human bodies according to these boxes; performing multi-camera fusion on the human body target detection boxes and the human head target detection boxes to obtain the human body boxes and head boxes; determining the relative position relationship between the human bodies or the human heads and the water surface; calculating the correlation between the bounding box sequence at the current moment and that at the previous moment to obtain the human tracking trajectory; updating the state database; generating alarm information according to the state database.

Systems and techniques are described herein for performing optical flow estimation for one or more frames. For example, a process can include determining an optical flow prediction associated with a plurality of frames. The process can include determining a position of at least one feature associated with a first frame and determining, based on the position of the at least one feature in the first frame and the optical flow prediction, a position estimate of a search area for searching for the at least one feature in a second frame. The process can include determining, from within the search area, a position of the at least one feature in the second frame

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a driving scenario machine learning network and providing a simulated driving environment. One of the operations is performed by receiving video data that includes multiple video frames depicting an aerial view of vehicles moving about an area. The video data is processed and driving scenario data is generated which includes information about the dynamic objects identified in the video. A machine learning network is trained using the generated driving scenario data. A 3-dimensional simulated environment is provided which is configured to allow an autonomous vehicle to interact with one or more of the dynamic objects.

The present disclosure relates to a system for predicting a near future location of an object, which predicts a near future location of a dynamic object, through learning for sampling a sample obtained by estimating a location of the dynamic object by learning an image at current timing, which is captured by a camera, based on artificial intelligence and for changing the sample of the dynamic object whose location has been estimated into a Gaussian mixture model (GMM) by using history data.

A trajectory and a flight distance of a target moving body such as a ball hit by a golf club in an athletic competition such as a golf game may be calculated from the images having been taken. The calculation may be conducted from the pictures having been taken with a monocular camera of low resolution such as a smartphone. An initial velocity and a hitting angle of a target moving body such as a ball having been hit high by the golf club may be calculated.

A method, a computer program product, and a computer system for classifying bacteria. The method comprises extracting a morphology signature corresponding to one or more bacteria and extracting a motility signature corresponding to the one or more bacteria. The method further comprises merging the morphology signature and the motility signature into a merged vector signature and classifying the one or more bacteria based on the merged vector signature.

A method for stabilization of a video sequence captured by an electronic device is provided. The method includes identifying a subject in the video sequence, estimating a velocity of the subject relative to the electronic device, determining a point of view of a subject in the video sequence with respect to the electronic device and the velocity of the subject relative to the electronic device and stabilizing the video sequence based on the determined point of view.