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Faster-RCNN (Recurrent Convolutional Neural Network) and Kalman filtering combined moving human body tracking method

A Kalman filter, human body technology, applied in the field of image processing, can solve the problems of tracking failure, affecting the real-time tracking of moving human bodies, etc., to achieve the effect of improving real-time performance, high tracking accuracy, and strong robustness

Active Publication Date: 2019-10-11
HARBIN ENG UNIV
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AI Technical Summary

Problems solved by technology

The method based on Faster-RCNN is the most mainstream method for moving human body tracking, but this method uses a fully connected layer for classification when performing human body classification, and the network parameters exceed one million, which seriously affects the real-time performance of moving human body tracking.
And this method is not suitable for the situation where the moving body is occluded. When the moving body is occluded, the tracking will fail

Method used

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  • Faster-RCNN (Recurrent Convolutional Neural Network) and Kalman filtering combined moving human body tracking method
  • Faster-RCNN (Recurrent Convolutional Neural Network) and Kalman filtering combined moving human body tracking method
  • Faster-RCNN (Recurrent Convolutional Neural Network) and Kalman filtering combined moving human body tracking method

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Embodiment

[0059] (1) Simplification of Faster-RCNN network

[0060] The original Faster-RCNN network generally includes three parts: the convolutional neural network, the RPN network and the fully connected layer. Since the fully connected layer contains millions of parameters when performing feature classification, the running speed is slow, and it does not meet the real-time requirements of moving human body tracking in a dynamic background. Therefore, it is necessary to remove the fully connected layer when simplifying the Faster-RCNN network. part.

[0061] 1.1) Acquisition of convolutional feature maps

[0062] Before the input image enters the convolutional neural network, in order to ensure the consistency of the output vector, it needs to be resized, and all input images are resized to 800×600. Then send the picture to the trained convolutional neural network (VGG16) to get a complete convolutional feature map of the input image.

[0063] 1.2) Acquisition of moving human body...

Embodiment approach

[0099] The embodiment of the present invention comprises the following steps:

[0100] (1) Simplification of Faster-RCNN network

[0101] The original Faster-RCNN network generally includes three parts: the convolutional neural network, the RPN network and the fully connected layer. Since the fully connected layer contains millions of parameters when performing feature classification, the running speed is slow, and it does not meet the real-time requirements of moving human body tracking in a dynamic background. Therefore, it is necessary to remove the fully connected layer when simplifying the Faster-RCNN network. part.

[0102] 1.1) Acquisition of convolutional feature maps

[0103] Before the input image enters the convolutional neural network, in order to ensure the consistency of the output vector, it needs to be resized, and all input images are resized to 800×600. Then send the picture to the trained convolutional neural network (VGG16) to get a complete convolutiona...

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Abstract

The invention discloses a Faster-RCNN (Recurrent Convolutional Neural Network) and Kalman filtering combined moving human body tracking method. The method comprises the following steps: firstly, simplifying the Faster-RCNN algorithm, leaving a convolutional neural network and an RPN network, and outputting a moving human body candidate position of the input image through the two networks; then, improving a classic Kalman filtering algorithm, changing a noise covariance matrix defined as a constant matrix in an original algorithm into a time-varying matrix, expanding an original state vector from four dimensions to eight dimensions, and increasing width, height and width and height change rate information of a moving human body position frame in the state vector; and finally, taking the obtained candidate positions of the moving human body as observed values of a Kalman filtering algorithm, obtaining estimated values of a plurality of positions of the moving human body in combination with predicted values of the Kalman filtering algorithm, and solving an average value through least square fitting, outlier removal and residual position removal to obtain optimal estimation of the positions of the moving human body. According to the method, the effect of accurately tracking the moving human body under the dynamic background is achieved.

Description

technical field [0001] The invention relates to a moving human body tracking method, in particular to a moving human body tracking method using Faster-RCNN combined with Kalman filtering, and belongs to the technical field of image processing. Background technique [0002] With the gradual transformation of social and economic types, human hands are gradually liberated from mechanical labor, and a large amount of mechanical labor is performed by robots. The development of mobile robots has experienced from the initial remote control driving and handling of various goods, to the current automatic tracking that can provide tracking services for specific personnel. The detection and tracking technology of the target human body is its main technical advancement point. In recent years, digital image processing technology has become more and more mature, and the target human body tracking technology based on visual image processing has become a research hotspot of many scholars an...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06T7/246
CPCG06T7/246G06N3/045G06F18/213
Inventor 苏丽朱伟张智朱齐丹秦绪杰
Owner HARBIN ENG UNIV
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