Fingertip tracking method based on deep learning and K-curvature method

A deep learning and curvature technology, applied in the field of fingertip tracking based on deep learning and K-curvature method, can solve the problems of difficult fingertip detection and tracking, high time complexity, poor robustness, etc., to improve accuracy performance and effectiveness, improving detection speed, increasing accuracy and robustness

Active Publication Date: 2021-11-05
HARBIN ENG UNIV
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Problems solved by technology

[0003] In the fingertip detection and tracking technology, the correctness of fingertip detection and the speed and accuracy of tracking are very important; at present, the algorithms based on target detection are mainly divided into traditional detection algorithms and detection algorithms based on deep learning. Detection algorithms mainly include DPM (Deformable Parts Model), selective search, etc. These methods have shortcomings such as high time complexity, poor robustness and poor generalization due to complex environments in practical applications. Occluded fingertips are difficult to detect and track effectively
[0004] The fingertip detection and tracking method based o

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  • Fingertip tracking method based on deep learning and K-curvature method
  • Fingertip tracking method based on deep learning and K-curvature method

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[0062] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0063] see figure 1 , is the overall network structure diagram of the present invention. First, use the YOLOv3 network model to train the preprocessed data set to obtain the fingertip detection model; then use the camera to obtain the video stream, input the detection model and detect the detection frame information, and initialize the Kalman filter; then use the Kalman filter to obtain predictions frame, calculate the IOU of the frame detection frame and prediction frame, set the IOU threshold, and judge whether the IOU is greater than the IOU threshold, if the IOU is greater than the IOU threshold, update the Kalman filter to obtain the fingertip tracking frame; otherwise, use K- The curvature method corrects the position of the fingertip and updates the Kalman filter; finally, a time threshold T-max is set, and if no tracking information is...

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Abstract

The invention discloses a fingertip tracking method based on deep learning and a K-curvature method, and the method comprises the steps: training a preprocessed data set through a YOLOv3 network model, and obtaining a fingertip detection model; acquiring a video stream by using a camera, inputting the video stream into a detection model, detecting detection frame information, and initializing a Kalman filter; then obtaining a prediction frame by using a Kalman filter, calculating IOU of the detection frame and the prediction frame of the frame, setting an IOU threshold value, judging whether the IOU is greater than the IOU threshold value or not, and if the IOU is greater than the IOU threshold value, updating the Kalman filter to obtain a fingertip tracking frame; otherwise, correcting the fingertip position by using a K-curvature method, and updating the Kalman filter; and finally, setting a time threshold T-max, and stopping tracking if tracking information is not detected in a time threshold frame. The influence of a complex environment on the detection accuracy is weakened, the detection speed is increased, and the accuracy and robustness are improved.

Description

technical field [0001] The invention belongs to target detection and tracking technology, in particular to a fingertip tracking method based on deep learning and K-curvature method. Background technique [0002] Hand posture detection and tracking is a popular direction in the field of human-computer interaction and computer vision. Its sub-direction fingertip detection and tracking technology is an important part of human hand posture detection and tracking technology. By detecting and tracking fingertips, it can Human-computer interaction behaviors such as handwriting, clicking on virtual screens in the air, gesture recognition, and smart teaching provide a good foundation. [0003] In the fingertip detection and tracking technology, the correctness of fingertip detection and the speed and accuracy of tracking are very important; at present, the algorithms based on target detection are mainly divided into traditional detection algorithms and detection algorithms based on d...

Claims

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Application Information

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IPC IPC(8): G06F3/0488G06K9/46G06N3/04G06N3/08
CPCG06F3/04883G06N3/08G06N3/045
Inventor 孟浩王玥田洋邓艳琴
Owner HARBIN ENG UNIV
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