Video pedestrian recognition method based on convolution neural network

Abstract

The present invention discloses a video pedestrian recognition method based on a convolution neural network. The method comprises a step of reading the video in a video database, intercepting a video frame, and extracting the HOG feature of the video frame, a step of constructing and training the convolution neural network, a step of selecting a plurality of character feature attributes and designing a support vector machine classifier for each character feature attribute and carrying out training, a step of inputting the HOG feature into a trained convolution neural network model, and carrying out sorting classification on each character feature . The method has the advantages that the method of the convolution neural network is employed to reflect a recognition rate well, the HOG feature is extracted, thus the amount of calculation is reduced, the speed is improved, the constructed convolution neural network has a certain depth, at the same time combined with a support vector machine, the classification is carried out for multiple times, and the recognition efficiency and accuracy are improved greatly.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a video pedestrian recognition method based on a convolutional neural network. Background technique [0002] With the development of multimedia technology and Internet technology, video pedestrian recognition is also a popular research object in the field of computer vision in recent years, and has broad application prospects in intelligent transportation, people tracing, and security. A traditional recognition algorithm for video pedestrian recognition is artificial neural network, which abstracts the human brain neuron network from the perspective of information processing and establishes a simple model. The training algorithm based on the artificial neural network is the backpropagation algorithm, which enables the network model to obtain statistical laws through the process of learning a large number of training samples, so as to make predictions for unknown eve...

AI Technical Summary

Problems solved by technology

The advantage of artificial neural network is that it has strong nonlinear mapping ability, self-learning and self-adaptive ability, generalization ability and certain fault tolerance ability, but it has the following disadvantages, the convergence speed is slow in the training of pedestrian recognition samples, and its training process is The supervision process, but the labeling of training samples is time-consuming and laborious, and the video pedestrian recognition involves the calculation and analysis of a large amount of data, plus the interference of some environmental factors, the traditional recognition algorithm cannot extract the preferred features of the image, resulting in a limited recognition rate

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