HOG (Histogram of Oriented Gradient) and Mean Shift algorithm-based indoor pedestrian detection and tracking method

A pedestrian detection and algorithm technology, which is applied in computing, computer parts, character and pattern recognition, etc., can solve the problems of Nao robot, such as limited field of vision, inability to detect, and various postures, so as to achieve good reference value, good practicability, Good real-time and robust effects

Inactive Publication Date: 2017-07-07
BEIJING UNIV OF TECH
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Problems solved by technology

Therefore, the field of vision of the Nao robot is limited, and if the human body leaves the field of vision of the robot, it will not be able to detect
Moreover, the human body is a non-rigid body, and its posture

Method used

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  • HOG (Histogram of Oriented Gradient) and Mean Shift algorithm-based indoor pedestrian detection and tracking method
  • HOG (Histogram of Oriented Gradient) and Mean Shift algorithm-based indoor pedestrian detection and tracking method
  • HOG (Histogram of Oriented Gradient) and Mean Shift algorithm-based indoor pedestrian detection and tracking method

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Embodiment Construction

[0036] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0037] figure 1 It is the overall architecture diagram of the system. The principle is to connect the computer and the robot through the Chrograph software first. After the connection is successful, the robot is in a rigid state. It will adjust the joints in time and traverse the field of view that can be observed. Move the observed human body to the center of the robot's field of view for easy detection. Then calculate the distance between the robot and the human body to locate the target. Then the human body is detected through the HOG+SVM algorithm. At this time, the detected human body image will be framed out in time. When the human body moves, the improved key will be triggered, then the video sequence will be recorded, and then converted into an image of each frame, and then the human body in the image will be detected one by one, and ...

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Abstract

The invention discloses an HOG (Histogram of Oriented Gradient) and Mean Shift algorithm-based indoor pedestrian detection and tracking method and belongs to the technical field of pedestrian detection and tracking in indoor complex environments. The method includes the following steps of: positioning of a Nao robot and a human body; HOG algorithm feature extraction; pedestrian detection HOG as well as SVM training and verification; Mean Shift algorithm target tracking; and Nao robot platform-based verification. According to the indoor pedestrian detection and tracking method of the invention, the HOG algorithm and the Mean Shift algorithm are adopted, and therefore, pedestrians can be detected in real time, and detected pedestrians can be tracked. Compared with methods in an early stage which perform pedestrian detection through Haar features, the indoor pedestrian detection and tracking method of the invention has a higher detection success rate and can reach a detection success rate of 90% in an INRIA pedestrian image database where backgrounds are complex. The indoor pedestrian detection and tracking method of the invention has the advantages of real-time performance, accurate detection and good practicability.

Description

technical field [0001] The invention belongs to the technical field of human body detection and tracking in an indoor complex environment, specifically uses a Nao robot as a platform, adopts a detection and tracking algorithm, and realizes the purpose of intelligent, fast and real-time detection and tracking of a human body. Background technique [0002] With the rapid development of computer vision technology, while people enjoy the convenience it brings in their daily life, they are also researching and exploring artificial intelligence to give robots the functions of human vision, making it more convenient, real-time and faster , so that the robot can handle the problem itself. In recent years, with the rapid development of digital image processing, pattern recognition, artificial intelligence, machine learning and other technologies, as well as the urgent needs of intelligent monitoring, intelligent vehicles and intelligent security, pedestrian detection and tracking tec...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06T7/246
CPCG06T2207/10016G06V40/103G06V10/507G06F18/2411
Inventor 李建更张岩左国玉李立杰王朋飞
Owner BEIJING UNIV OF TECH
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