The invention provides a behavior action detection method, system and device and a medium. The method comprises the following steps: intercepting one or more frames of images from a video stream; performing human body frame labeling and behavior category labeling on the one or more frames of images, and cutting out corresponding image regions from the one or more frames of images according to thelabeled human body frame coordinates; taking the cut image area with the human body frame label and the behavior category label as training data, training a classification neural network based on thetraining data, and generating a behavior classification model; and detecting the to-be-detected image by utilizing the behavior classification model, and determining whether a target behavior action exists in the to-be-detected image or not. According to the method, the deep learning algorithm is used for detecting the crossing behavior, so that the false detection rate and the missing detection rate can be greatly reduced. Moreover, the method can be applied to multiple scenes, targeted learning and distinguishing can be carried out according to behaviors of people near the railing, whether the people hold the railing or climb over the railing can be judged more accurately, and the false alarm rate of the method can be smaller than 1%.

Behavior action detection method, system and device and medium

An action recognition method. The method comprises: acquiring original feature sub-images of image data of each timing frame in different convolutional channels by means of a multi-channel convolutional layer; respectively taking each timing frame as a target timing frame, calculating a motion information weight of the target timing frame in each convolutional channel according to an original feature sub-image of the target timing frame in each convolutional channel and an original feature sub-image of the next timing frame in each convolutional channel, and acquiring a motion information feature image of the target timing frame in each convolutional channel according to the motion information weight; performing timing convolution on the motion information feature image of the target timing frame in each convolutional channel to obtain a timing motion feature image of the target timing frame in each convolutional channel; and recognizing the action type of a moving object in the image data of the target timing frame according to the timing motion feature image of the target timing frame in each convolutional channel.

Action recognition method and apparatus, computer storage medium, and computer device

The invention discloses a time sequence event action detection method. The method includes: taking each video frame in the whole video as a key frame; selecting associated time sequence fragments before and after or nearby; according to the length of the selected time sequence segment, building a P3D convolutional neural network with a variable time sequence; ensuring the output of the 3D convolutional network to be a 2D action feature map; ignoring the tiny displacement change of the time sequence associated fragment; assuming that the position is approximately the same as the key frame; finding a candidate prediction box of the key frame on the action feature map through a candidate area network; locking the action range size, selecting corresponding action features; carrying out ROIAlign normalization to obtain the action features with the same size and size; sending the action features to a classification and regression network of a Faster RCNN; and obtaining a final required action detection result. Modeling is carried out on action features in a time sequence event, the quality of the action features used by RPN extraction candidate areas in a Faster RCNN is improved througha 3DCNN, then the accuracy of action detection regression is improved, then the action features are used for action recognition in the Faster RCNN, and the accuracy of action detection classificationcan also be improved.

Time sequence event action detection method

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Time sequence event action detection method