Real-time vehicle flow detecting and tracking method based on aerial photography data

A real-time traffic flow and data technology, applied in image data processing, image analysis, image enhancement, etc., can solve the problems of poor robustness, image information loss, etc., achieve the balance between accuracy and time, and improve the effect of detection accuracy

Active Publication Date: 2018-11-30
HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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Problems solved by technology

[0004] (1) The image information loss of the binary image obtained by the inter-frame difference method, this technology can easily le

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  • Real-time vehicle flow detecting and tracking method based on aerial photography data
  • Real-time vehicle flow detecting and tracking method based on aerial photography data
  • Real-time vehicle flow detecting and tracking method based on aerial photography data

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[0041] The present invention will be further described below in conjunction with the description of the drawings and the specific embodiments.

[0042] A real-time traffic flow detection and tracking method based on aerial photography data includes the following steps:

[0043] S1. Based on the pre-training part of weakly supervised learning, use weakly supervised learning to train a pre-training model of the YOLO network;

[0044] S2, the real-time traffic flow detection part based on aerial photography data, adopts the full convolutional neural network and the multi-target frame detection method with prior information to improve the pre-training model of the YOLO network to obtain the YOLO detection network;

[0045] S3. In the multi-view and multi-resolution training part, a multi-view and multi-resolution training method is used to train on the YOLO detection network to obtain a detection model;

[0046] S4. The matching vehicle flow tracking part uses the detection model to detect ...

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Abstract

The invention provides a real-time vehicle flow detecting and tracking method based on aerial photography data. The method comprises the following steps that 1, a pre-training model of a YOLO networkis trained on the basis of a pre-training part of weak supervised learning by using a weak supervised learning mode; and 2, the pre-training model of the YOLO network is improved on the basis of a real-time vehicle flow detecting part of aerial photography data by adopting a full convolutional neural network and a multi-target box detection method with prior information to obtain a YOLO detectingnetwork. The method has the advantages that improvement is achieved on the basis of a YOLO algorithm, the full convolutional neural network and the multi-target box detection method with the prior information are adopted, training is conducted by effectively utilizing the multi-view and multi-resolution image characteristics of an aerial photography data set of an unmanned aerial vehicle, the detecting accuracy rate of the algorithm is increased on the condition of not losing too much detecting time, and the balance of the accurate rate and the time is achieved.

Description

technical field [0001] The invention relates to vehicle flow detection, in particular to a real-time vehicle flow detection and tracking method based on aerial photography data. Background technique [0002] At present, the relatively mature traffic detection technology is mainly based on the inter-frame difference method. First, the video is converted into an image sequence and processed in grayscale. The difference image is obtained by the inter-frame difference method, and then the difference image is filtered, binarized and morphological. Finally, the contour detection algorithm is used to detect and track the vehicle. [0003] The method of directly detecting vehicles based on pixel intensity changes in aerial video data has the advantage of good accuracy, but it has the following shortcomings due to theoretical limitations: [0004] (1) The image information loss of the binary image obtained by the inter-frame difference method is easy to cause missed and repeated det...

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Application Information

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IPC IPC(8): G06T7/246
CPCG06T2207/10016G06T2207/10032G06T2207/20081G06T2207/20084G06T2207/30232G06T2207/30236G06T7/248
Inventor 叶允明夏武张晓峰项耀军
Owner HARBIN INST OF TECH SHENZHEN GRADUATE SCHOOL
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