A method and device for calculating a river surface flow velocity are provided based on a variational principle, which are used to capture and process the images of an objective area, and to obtain the flow velocity field data of the objective area with high precision in a non-contact manner. The method and device include 3 steps: (1) preparation before initial flow measurement; (2) capturing a video of the river by an image acquisition device, converting a motion of a pixel flow field of the fluid in a captured image sequence into solving an energy functional optimization problem, and solving partial differential equations to obtain data of pixel flow field distribution; and (3) obtaining space coordinates of the pixel point in a world coordinate system and calculating the flow velocity according to the data obtained in the step 2 and the transformation relationship determined in the step 1.

The invention relates to a method for recognizing a tilting movement of a vehicle having at least one camera installed on the vehicle The method includes recording an image sequence by the camera, determining an optical flow of the image sequence, determining an optical flow to be expected on the grounds of a travel movement of the vehicle and a topography of the underlying surface, and checking of the optical flow of the image sequence and of the expected optical flow for deviations. A vehicle adapted to perform the method is also disclosed.

Provided herein are systems, methods, and media capable of determining estimated force applied on a target tissue region to enable tactile feedback during interaction with said target tissue region.

Optical flow information is determined and used to identify a video clip from among a sequence of frames. The video clip may be identified based on motion features derived in part from the optical flow information. In some embodiments, semantic information is concatenated with the motion features derived from the optical flow information.

The present disclosure relates to a method and an apparatus for detecting a subject, an electronic device, and a computer readable storage medium. The method includes the following. A current image and a previous image are obtained. A transformation matrix between the current image and the previous image is obtained in response to determining that the current image indicates the shaking state. The previous image is corrected based on the transformation matrix. The subject detection model is updated based on the corrected previous image. The subject detection is performed on the current image based on the updated subject detection model, to obtain a target subject.

The present disclosure relates to a method and an apparatus for detecting a subject, an electronic device, and a computer readable storage medium. The method includes the following. A current image and a previous image are obtained. A transformation matrix between the current image and the previous image is obtained in response to determining that the current image indicates the shaking state. The previous image is corrected based on the transformation matrix. The subject detection model is updated based on the corrected previous image. The subject detection is performed on the current image based on the updated subject detection model, to obtain a target subject.

The present disclosure provides an optical flow calculation method for a computing device, including: acquiring an event data flow with a predetermined duration from a DVS, the event data flow including a coordinate position and a timestamp of a triggered event; generating a timestamp matrix in accordance with the coordinate position and the timestamp of the triggered event; scanning elements in the timestamp matrix in a predetermined scanning direction, so as to determine at least one intermediate point in each element in accordance with a value and a gradient of the element in the predetermined scanning direction; and calculating a distance between adjacent intermediate points and a gradient direction, and generating an optical flow matrix in accordance with a calculation result. The present disclosure further provides the computing device.

The present disclosure provides an optical flow calculation method for a computing device, including: acquiring an event data flow with a predetermined duration from a DVS, the event data flow including a coordinate position and a timestamp of a triggered event; generating a timestamp matrix in accordance with the coordinate position and the timestamp of the triggered event; scanning elements in the timestamp matrix in a predetermined scanning direction, so as to determine at least one intermediate point in each element in accordance with a value and a gradient of the element in the predetermined scanning direction; and calculating a distance between adjacent intermediate points and a gradient direction, and generating an optical flow matrix in accordance with a calculation result. The present disclosure further provides the computing device.

An electronic device having a neural network framework for estimating optical flow is provided. The electronic device is connected to an image acquiring unit, which acquires images to be analyzed. The electronic device includes a storage unit, a feature extraction unit, an optical flow estimation unit and a refining unit. The storage unit stores a feature extraction module. The feature extraction unit is connected to the image acquiring unit and the storage unit. The optical flow estimation unit is connected to the feature extraction unit to generate an estimated optical flow. The refining unit is connected to the optical flow estimation unit to input the estimated optical flow to a refining module to obtain an estimated optical flow result. A method for estimating optical flow is also provided to reduce the number of training parameters required for estimating optical flow, thereby reducing a training time and improving training stability.

A learning method and a learning device for visual odometry based on an ORB feature of an image sequence are provided. The learning method includes: recording images, and constituting an original data set by means of the plurality of obtained images; performing ORB feature extraction on the images in the original data set to realize extraction of first key features; performing feature extraction and matching on continuous images in the original data set by means of a convolutional neural network, and extracting rich second key features from the sequential images; and inputting the first key features and the second key features extracted from the original data set into a multi-layer long-short-term memory network for training and learning, and generating and outputting estimation of a visual odometer. Rich first key features are extracted from an image sequence, and then a tracking algorithm is used for tracking the features in continuous frames.