Fast pedestrian detection method and device

Abstract

The invention discloses a fast pedestrian detection method and device. The method comprises the following steps: step S1, constructing a configurable deep model based on a convolutional neural network, and utilizing training samples to obtain parameters of a constructed network by learning to obtain a model used for a test process; and step S2, inputting test samples, utilizing a variation law ofneural-network perception domains, using different intermediate layers to detect target objects in different scale ranges through the trained model, and obtaining box graphs of the target objects in images by prediction. The method uses the different intermediate layers to detect the target objects in the certain scale ranges through utilizing the variation law of the neural-network perception domains, better adapts to relationships of the perception domains and object sizes, and effectively improves a detection result.

Description

technical field [0001] The invention relates to the technical field of pedestrian detection, in particular to a deep learning-based fast pedestrian detection method and device for embedded systems. Background technique [0002] As a part of object detection in computer vision, pedestrian detection is of great significance in real-world applications. With the maturity of image acquisition technology and the decline of storage technology costs, more and more cameras are deployed in public places. On the other hand, With the implementation of autonomous driving and intelligent transportation, car cameras have also produced massive video resources. Traditional manual screening and processing is not only inefficient and consumes a lot of manpower and material resources, but also may introduce some human factors, resulting in some deviations. In recent years, deep learning has made unprecedented breakthroughs in the field of computer vision. Not only is the efficiency far superio...

AI Technical Summary

Problems solved by technology

Traditional manual screening and processing is not only inefficient and consumes a lot of manpower and material resources, but also may introduce some human factors, resulting in some deviations

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