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Vehicle recognition and tracking method based on convolutional neural networks

A convolutional neural network and vehicle recognition technology, which is applied in biological neural network models, neural architectures, character and pattern recognition, etc., can solve complex scenes and tricky angles that are difficult to apply, difficult to meet time characteristic requirements, and applicable to a single scene and other problems, to achieve the effect of being easy to realize by computer, saving calculation overhead, and having strong nonlinear mapping ability

Active Publication Date: 2018-06-15
XIDIAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method is applicable to a very single scene, and it is difficult to apply to complex scenes and tricky angles
[0005] In the existing tracking and recognition technology, the template matching method is used, although it is relatively fast, there is a high probability of recognition errors for objects that are not in the template; while the binary classification method is used, due to the use of machine learning methods, the recognition accuracy is extremely high , but it takes a long time for pre-preparation and training, and it is difficult to meet the time characteristic requirements in the tracking process

Method used

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  • Vehicle recognition and tracking method based on convolutional neural networks
  • Vehicle recognition and tracking method based on convolutional neural networks
  • Vehicle recognition and tracking method based on convolutional neural networks

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Embodiment 1

[0041] In the existing tracking and recognition technology, the method of template matching is used to process the data very fast, but the detailed features in the class are often not accurately distinguished, and classification errors are prone to occur; while the binary classification method needs to select a suitable Features, and a classifier model is trained through a large number of training pictures. The detection results are often more accurate, but sufficient preparation is required, and it is difficult to meet the time characteristic requirements during the tracking process.

[0042] At present, the recognition and tracking of objects in surveillance video images is of great significance for traffic congestion mitigation, vehicle speed measurement, and emergency traffic incident handling. To deal with the above traffic problems, it is necessary to adopt relatively fast and accurate tracking and identification technology. However, in terms of time characteristics and ...

Embodiment 2

[0069] The vehicle recognition and tracking method based on the convolutional neural network is the same as embodiment 1, the construction of the fast regional convolutional neural network described in the step (1b) of the present invention, see figure 2 , The structure of the fast regional convolutional neural network is, in turn, convolutional layer conv1, pooling layer pool1, convolutional layer conv2, pooling layer pool2, convolutional layer conv3, pooling layer pool3, convolutional layer conv4, pooling layer pool4, convolutional layer conv5, convolutional layer rpn_conv, convolutional layer rpn_cls_score, convolutional layer rpn_bbox_pred, region of interest pooling layer roi_pool, fully connected layer fc6, fully connected layer fc7, fully connected layer fc8, classification layer cls_prob, coordinates Layer bbox_pred.

Embodiment 3

[0071] The vehicle recognition and tracking method based on convolutional neural network is the same as embodiment 1-2, and the steps of the fast regional convolutional neural network constructed as described in step (1b) are as follows:

[0072] (1b.1), input a monitoring image of any size into the convolution layer conv1, use 64 convolution kernels, perform a convolution operation with a block size of 3×3 pixels and a step size of 1 pixel, and obtain 64 channels feature map;

[0073] (1b.2), input the 64-channel feature map output by the convolutional layer conv1 to the pooling layer pool1 to obtain a 64-channel feature map;

[0074] (1b.3), input the 64-channel feature map output by the pooling layer pool1 to the convolutional layer conv2, and use 128 convolution kernels to perform convolution with a block size of 3×3 pixels and a step size of 1 pixel Operation, get 128 channel feature map;

[0075] (1b.4), input the 128-channel feature map output by the convolutional lay...

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Abstract

The invention discloses a vehicle recognition and tracking method based on convolutional neural networks. Through the method, the problem that it is difficult to guarantee instantaneity under a high-precision condition in the prior art is solved, and the defects of inaccurate classification results, long tracking and recognition time and the like are overcome. The method comprises the implementation steps that a quick region convolutional neural network is constructed and trained; an initial frame of a monitoring video is processed and recognized; a tracking convolutional neural network is trained off line; an optimal candidate box is extracted and selected; a sample queue is generated; online iterative training is performed; and a target image is acquired, and instant vehicle recognitionand tracking are realized. According to the method, a Faster-rcnn and the tracking convolutional neural network are combined, and high-level features with good robustness and high representativeness of vehicles are extracted by use of the convolutional neural networks; through network fusion and an online-offline training alternating mode, time needed for tracking and recognition is shortened on the basis of guaranteeing high precision; the recognition result is accurate, and tracking time is shorter; and the method can be used for cooperating with an ordinary camera to complete instant recognition and tracking of the vehicles.

Description

technical field [0001] The invention belongs to the field of image processing technology, and further relates to computer image processing technology, specifically a convolutional neural network-based vehicle identification and tracking method, which can be used to identify and track objects in surveillance videos and images of any size. Background technique [0002] At present, vehicle identification and tracking based on road traffic monitoring video images has become a very important application and a research topic in the field of intelligent identification and monitoring technology. According to the different methods of object traversal and screening in the image, traditional object detection methods are mainly divided into two categories: one is based on template matching; the other is based on appearance features of binary classification methods. The method of matching and screening based on the simplified vehicle template is usually very fast, but the simplified vehi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/52G06V2201/08G06N3/045G06F18/24G06F18/214
Inventor 宋彬康煦孙峰瑶秦浩
Owner XIDIAN UNIV
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