3118 results about "Optical flow" patented technology

A panoramic image is generated from a sequence of input frames captured by a camera that translates relative to a scene having at least two points at different distances from the camera. A processor (13) is responsive to optical flow between corresponding points in temporally different input frames for computing flow statistics for at least portions of some of the input frames and for computing respective stitching costs between some of the portions and respective neighboring portions thereof. A selection unit (18) selects a sequence of portions and respective neighboring portions that minimizes a cost function that is a function of the flow statistics and stitching costs. A stitching unit (21) stitches the selected portions and respective neighboring portions so as to form a panoramic image of the scene, which may then be displayed or post-processed.

The invention discloses a bidirectional long short-term memory unit-based behavior identification method for a video. The method comprises the steps of (1) inputting a video sequence and extracting an RGB (Red, Green and Blue) frame sequence and an optical flow image from the video sequence; (2) respectively training a deep convolutional network of an RGB image and a deep convolutional network of the optical flow image; (3) extracting multilayer characteristics of the network, wherein characteristics of a third convolutional layer, a fifth convolutional layer and a seventh fully connected layer are at least extracted, and the characteristics of the convolutional layers are pooled; (4) training a recurrent neural network constructed by use of a bidirectional long short-term memory unit to obtain a probability matrix of each frame of the video; and (5) averaging the probability matrixes, finally fusing the probability matrixes of an optical flow frame and an RGB frame, taking a category with a maximum probability as a final classification result, and thus realizing behavior identification. According to the method, the conventional artificial characteristics are replaced with multi-layer depth learning characteristics, the depth characteristics of different layers represent different pieces of information, and the combination of multi-layer characteristics can improve the accuracy rate of classification; and the time information is captured by use of the bidirectional long short-term memory, many pieces of time domain structural information are obtained and a behavior identification effect is improved.