A method and apparatus for image recognition

Abstract

The embodiment of the invention provides an image recognition method and a device. The method comprises the following steps: obtaining an image to be recognized, dividing the image to be recognized into a plurality of sub-images according to a preset size, and obtaining a first gray feature vector corresponding to each sub-image; acquiring a second gray feature vector corresponding to the matchingtemplate, calculating similarity between the sub-image and the matching template according to the first gray feature vector and the second gray feature vector; the sub-images whose similarity is greater than the preset threshold are selected as the target sub-images, and the target sub-images are recognized by the classification model according to the target sub-images. The apparatus is configured to perform the method. An embodiment of the present invention calculates a similarity between a sub-image and a matching template according to a first gray feature vector and a second gray feature vector, and the target sub-images whose similarity is greater than the preset threshold are selected as the target sub-images, and then the target sub-images are input into the classification model forrecognition, thereby reducing the recognition time and improving the recognition efficiency of the images to be recognized.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of image processing, and in particular, to an image recognition method and device. Background technique [0002] With the overall development of the economy and society, out of safety considerations, the safety monitoring of key areas such as residential areas has been fully carried out. Installing cameras in key areas for security monitoring is currently the most effective security measure. Through the camera to collect images of pedestrians, detect some dangerous situations in time, generate early warnings and send out abnormal signals in real time, which can not only reduce the monitoring cost of manpower, but also improve the accuracy and real-time performance of monitoring, thereby improving the safety of key areas. security situation. [0003] Taking residential quarters as an example, at present, high-definition cameras with two resolutions of 720P and 1080P are widely used in ...

AI Technical Summary

Problems solved by technology

[0005] In practical applications, the target to be detected often appears at any position in the picture. In order not to miss any area during the detection process, it is necessary to traverse the entire picture. The HOG feature uses the sliding window detection method to detect the target in an exhaustive manner. It traverses the picture from left to right and top to bottom with a fixed-size scanning frame. Each scanning frame is a sub-image to be detected. However, the number of windows to be detected in a picture is much smaller than that of the background window. The algorithm spends too much time in the feature extraction of tens of thousands of background windows, especially when high-resolution and high-speed cameras are commonly used in current community monitoring, the recognition efficiency of the image to be detected is very low, which cannot meet the current requirements. Real-time detection of pedestrians in the community under the state

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