A vehicle detection method based on GPU multi-core parallel acceleration

Abstract

The invention discloses a vehicle detection method based on GPU (ground power unit) multi-core parallel acceleration. The method comprises the following steps including computer visual sense and characteristic extraction, target similarity detection and Map-Reduce parallel calculation framework. The method has the beneficial effects that through the Map-Reduce GPU parallel calculation, the HOG (histograms of oriented gradients) characteristic extraction algorithm efficiency is improved, the time required by the vehicle detection is obviously shortened, and the method can be used in the field of automatic intelligent traffic and urban management.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a method for applying MapReduce-based GPU parallel computing to vehicle detection. Background technique [0002] With the continuous improvement of the importance of intelligent transportation systems in modern society, vehicle monitoring technology has been more and more widely used. The main problem of the existing intelligent transportation system is that a large amount of monitoring information is difficult to be processed effectively and in time. How to intelligently analyze and extract effective information in real time through computers to improve the accuracy of vehicle monitoring and tracking has become a hot research issue in the field of computer vision. [0003] Intelligent analysis technology based on visual monitoring is a hot and difficult issue in the field of computer vision and intelligent transportation, involving image processing, machine learning, pattern reco...

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Problems solved by technology

[0005] However, there are still some problems in these researches and applications: although the detection and tracking algorithms based on HOG features can achieve relatively ideal results, under the traditional computing architecture, the extraction of HOG features, the calculation of SVM training and the calculation of feature matching Both are time-consuming and far from meeting the real-time requirements

However, the GPU also has corresponding shortcomings, mainly including: (1) The original purpose of the GPU is to accelerate image processing, using the SIMD (Single Instruction Multiple Data) mode, which does not support complex control flow; (2) GPU threads It is managed by hardware; (3) GPU does not support direct access to host memory, and needs to be scheduled for host memory and graphics card memory; (4) There is no unified standard for GPU, which is often determined by each manufacturer

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