Level set target tracking method based on convolutional neural network

A convolutional neural network and target tracking technology, which is applied in image data processing, instrumentation, computing, etc., can solve the problems of low efficiency of tracking module algorithm, inability to obtain target outline, target detection, insufficient efficiency and accuracy of outline extraction, etc.

Inactive Publication Date: 2018-02-16
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Application Information

AI Technical Summary

Problems solved by technology

The system can obtain the tracking frame of the tracking target, but only the position and size of the target, not the specific outline of the target
[0010] The above-mentioned existing tracking algorithms have completed the task of target tracking to a certain extent, however, they have shortcomings in the efficiency and accuracy of target detection and contour extracti

Method used

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  • Level set target tracking method based on convolutional neural network
  • Level set target tracking method based on convolutional neural network
  • Level set target tracking method based on convolutional neural network

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Experimental program
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Embodiment 1

[0082] This embodiment illustrates the application of the present invention "a level set target tracking method based on convolutional neural network" to the video "cheetah" (available from the database: http: / / cpl.cc.gatech.edu / projects / SegTrack / The process of a certain frame in download):

[0083] figure 1 For the algorithm flow of this method and this embodiment, from figure 1 It can be seen that the method includes the following steps:

[0084] Step A: Initialize the tracking program;

[0085] Specifically in this embodiment, the input video frame I 1 For the first frame of "cheetah", the input contour C 1For the first frame standard segmentation result provided by the database, extract features and obtain Adaboost strong classifier. The features are divided into three parts:

[0086] 6 different channel features f of RGB (Red, Green, Blue) and HSV (Hue, Saturation, Value) color spaces 1 -f 6 , a total of 6 dimensions (n color = 6);

[0087] HOG feature f for 5×...

Embodiment 2

[0099] This embodiment specifically illustrates the tracking results obtained by executing steps 1 to 4 of the present invention on two video frames "cheetah" and "monkeydog" (the tracking outline is represented by a white line).

[0100] figure 2 Divided into 2 rows and 3 columns, each row represents a different frame of a video, and 3 columns represent the tracking results of the middle three frames of the video;

[0101] From figure 2 It can be seen from the figure that this method can accurately capture the outline of objects on a series of images and maintain high-quality tracking. The validity of the tracking method is illustrated.

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Abstract

The invention relates to a level set target tracking method based on a convolutional neural network, and belongs to the technical field of target tracking and video processing. The level set target tracking method based on a convolutional neural network includes the following steps: 1) utilizing the first frame and the first frame standard target contour of video input to initialize an Adaboost detection module; 2) calling a detection module to obtain the initial position and shape of an object in the subsequent video frame; 3) operating a level set method tracking module on the above basis, and accurately segmenting the object contour; and 4) by means of the result of the step 3, distinguishing the foreground/background, and updating a weak classifier of the detection module. For the level set target tracking method based on a convolutional neural network, the utilization method of convolutional neural network characteristics can make a preferable distinction on the foreground/background so as to improve the detection accuracy and improve the overall target tracking effect; and the level set target tracking method based on a convolutional neural network can balance performance andefficiency, and can obtain relatively excellent tracking effect at the same operating speed.

Description

technical field [0001] The invention relates to a level set target tracking method based on a convolutional neural network, and belongs to the technical fields of target tracking and video processing. Background technique [0002] Object tracking is a widely used technique. In the field of computer video processing, the target tracking problem generally refers to calculating the position or contour of the object in each subsequent frame given the position or contour information of the first frame in the video sequence. Object tracking remains a challenging problem due to problems such as deformation, occlusion, and scale transformation of objects in video sequences. In order to solve this problem, the TLD (Tracking-learning-detection) algorithm combines tracking and detection (Detection), laying the foundation for the tracking by detection (Tracking by Detection) algorithm. At present, more efficient algorithms are generally developed under this framework. They are mainly ...

Claims

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Application Information

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IPC IPC(8): G06T7/246
CPCG06T7/246G06T2207/10016G06T2207/20081G06T2207/20084
Inventor 刘利雄宁小东
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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