Vehicle model identification method based on pooling multi-scale depth convolution characteristics

Abstract

The invention discloses a vehicle model identification method based on pooling multi-scale depth convolution characteristics, comprising the following steps: extracting the depth convolution characteristic of each vehicle model image in a vehicle model database according to different scales, wherein the first scale is not processed; carrying out PCA dimension reduction of the depth convolution characteristics of the remaining scales; carrying out coding using a local characteristic aggregation descriptor; carrying out PCA dimension reduction again to get the characteristic representation of the current scale; cascade-pooling the characteristics of all the scales to get the final characteristic representation of the current image; training a linear support vector machine using the characteristic representation of the vehicle model images to get a vehicle model identification system; and for a vehicle to be identified, acquiring the characteristic representation of the vehicle, and importing the vehicle into the identification system to identify the model of the vehicle. The traditional depth convolution characteristic lacks of geometrical invariability, which limits vehicle model image classification and identification in a variable scenario. The problem is well solved by using pooling multi-scale depth convolution characteristics of images. The vehicle model identification method of the invention is of high practicability and robustness.

Description

technical field [0001] The invention belongs to the technical fields of image processing, pattern classification and recognition, and in particular relates to a vehicle recognition method based on pooled multi-scale deep convolution features. Background of the invention [0002] Traditional vehicle recognition technology includes vehicle detection and segmentation, feature extraction and selection, pattern recognition and other processing. This type of technology faces many difficulties: how to segment the complete target vehicle area in a complex background is the premise and basis of vehicle type recognition; how to select representative features from among the many features of the car and convert them into effective parameters It is also extremely important; after obtaining the characteristic parameters, how to correctly select and design the classifier also directly affects the accuracy of the final recognition. [0003] The concept of deep learning originated from arti...

AI Technical Summary

Problems solved by technology

However, it does not perform well in car model recognition, and the global convolutional neural network features lack geometric invariance, which limits the classification and matching of variable scenes.

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