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Confidence term filter target tracking method for enhancing multi-feature fusion

A technology of multi-feature fusion and target tracking, which is applied in the field of confidence item filter target tracking that strengthens multi-feature fusion, can solve the problem that the three-frame difference method is not robust, the Kalman filter is difficult to deal with occlusion problems, and it is difficult to adapt to the target Tracking scenarios etc.

Active Publication Date: 2020-05-12
ANHUI UNIVERSITY OF TECHNOLOGY
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AI Technical Summary

Problems solved by technology

However, the robustness of the three-frame difference method is not high, and the Kalman filter is difficult to deal with occlusion problems and other problems, and it is difficult to adapt to target tracking scenarios in complex environments.

Method used

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  • Confidence term filter target tracking method for enhancing multi-feature fusion
  • Confidence term filter target tracking method for enhancing multi-feature fusion
  • Confidence term filter target tracking method for enhancing multi-feature fusion

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Embodiment Construction

[0066] refer to figure 1 , a confidence filter target tracking method that strengthens multi-feature fusion of the present invention, comprises the following steps:

[0067] Step 1: Calibrate the internal and external parameters of the camera;

[0068] Step 2: Obtain the input object of the target, input the video sequence to be tracked, and mark the initial frame;

[0069] Step 3: shoot the video information of the tracked target object in the monitoring area in real time with the camera, and send it to the video data processing unit;

[0070] Step 4: Extract the feature of the target to be tracked by extracting the video sequence obtained from the video data processing unit, and introduce the FHOG feature in the filter model; wherein the calculation process of the FHOG feature is as follows:

[0071] Step 4.1 First use the Prewitt gradient operator and Perform convolution calculation on the video frame to obtain the vertical edge gradient component and the horizontal e...

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Abstract

The invention discloses a confidence term filter target tracking method for enhancing multi-feature fusion, and belongs to the technical field of target tracking. The target tracking method comprisesthe following steps: carrying out calibration processing on internal and external parameters of a camera; obtaining an input object of a target, inputting a to-be-tracked video sequence, and marking an initial frame; filming the video information of the tracked target object in the monitoring area in real time by using a camera, and transmitting the video information to a video data processing unit; introducing an FHOG characteristic filter, a color name characteristic filter and a background perception filter; and according to the maximum response peak value and the average peak related energy of the filter, adding linear fusion of the adaptive multi-feature response filter to obtain a fusion confidence feature. According to the invention, the aliasing effect problem of the filter model can be effectively solved, and compared with a traditional algorithm, the algorithm has better robustness.

Description

technical field [0001] The invention belongs to the technical field of target tracking, and more specifically relates to a confidence item filter target tracking method that strengthens multi-feature fusion. Background technique [0002] The tracking of specific objects in the video captured by the camera is a research hotspot in the field of computer vision. The purpose of target tracking is to monitor the position and size of the target object to be monitored in a continuous video sequence. Systems such as visual monitoring have a wide range in the field of robot vision. application prospects. [0003] Many researchers have conducted research on object tracking methods, and proposed generation methods for problems such as scale, fast motion, plane rotation, blur, occlusion, deformation, illumination changes, background clutter, out of view, and low resolution in videos. model-based and judgment-based model tracking methods. Among them, generative models mainly include sp...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06T7/80G06T7/90G06K9/00G06T7/246
CPCG06T7/80G06T7/90G06T7/246G06T2207/20056G06V20/46G06V20/48G06T5/70
Inventor 陶兆胜阮孟丽张敬寒周泳
Owner ANHUI UNIVERSITY OF TECHNOLOGY
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