Face tracking method based on improved Camshift algorithm

An algorithm and tracking algorithm technology, applied in computing, image data processing, instruments, etc., can solve the problems of large amount of calculation and easy interference of similar colors.

Active Publication Date: 2019-09-06
广州市凯泽利科技有限公司
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AI Technical Summary

Problems solved by technology

The target tracking algorithm determines the real-time performance and accuracy of target tracking, and the existing Camshift tracking algorithm has a large amount of calculations, which ensures the real-time performance of face windo

Method used

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  • Face tracking method based on improved Camshift algorithm
  • Face tracking method based on improved Camshift algorithm
  • Face tracking method based on improved Camshift algorithm

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Embodiment Construction

[0071] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0072] The invention provides a face tracking method based on the improved Camshift algorithm. Aiming at the situation that the Camshift tracking algorithm is easily disturbed by similar background features, cannot track fast and irregular moving objects, and has poor anti-occlusion ability, a predictive face tracking method based on improved Camshift and Kalman filter is proposed. This method has high real-time performance and strong robustness to background color interference.

[0073] Such as figure 1 Shown is a face tracking method based on the improved Camshift algorithm, which specifically includes the following steps:

[0074] Step 1. Taking the driver's face in the initial frame of the video image as the initial face window as the tracking target;

[0075] Step 2, model the improved Camshift tracking algorithm with the centroid position and window size of ...

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Abstract

The invention discloses a face tracking method based on an improved Camshift algorithm, and the method comprises the following steps: 1) taking a face in an initial frame of a video image as an initial face window, and taking the face as a tracking target; 2) modeling an improved Camshift tracking algorithm according to the centroid position and the window size of a tracking target, and performingcalculating to obtain an optimal candidate window in the current frame image by using the tracking algorithm; 3) calculating whether the shielding interference of the current frame image exceeds a given threshold; 4) when the interference is less than a given threshold, using the candidate window obtained in the step 2) as a target window, and returning to the target window; 5) if the interference is not less than a given threshold value, introducing a Kalman filter, carrying out predicting by taking the obtained target mass center as an observation vector, and returning a prediction result as a target window, and 6) taking an output prediction vector obtained by the Kalman filter as a search window center, and continuing target tracking of the next frame.

Description

technical field [0001] The technology relates to the technical field of video target tracking, in particular to a face tracking method based on an improved Camshift algorithm. Background technique [0002] In modern transportation operations, due to the long transportation distance and high work intensity, drivers are prone to fatigue driving, which leads to various traffic accidents. After using the convolutional neural network to complete the separation of the background and the face, and frame the face window, it is necessary to track the face window for subsequent judgment of fatigue through facial features. The target tracking algorithm determines the real-time performance and accuracy of target tracking, and the existing Camshift tracking algorithm has a large amount of calculations, which ensures the real-time performance of face window tracking. Since the size of the search window is not fixed, the tracking window continues to expand, similar to Colors are easily di...

Claims

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Application Information

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IPC IPC(8): G06T7/277G06T7/246
CPCG06T2207/30201G06T2207/30268G06T7/246G06T7/277
Inventor 姜立标李静轩张俊伟
Owner 广州市凯泽利科技有限公司
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