Gaze Detection Method Based on Local Evaluation and Global Optimization

A technology of global optimization and detection method, which is applied in the field of gaze point detection, to achieve the effect of improving the effect and the effect of good gaze point detection results

Inactive Publication Date: 2019-12-17
DALIAN UNIV OF TECH
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is: when the image contains one or more salient objects, when there are semantic information such as faces and text in the image, when there is no salient object in the image or the image scene is very complex, etc. Accurate prediction of human gaze point

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  • Gaze Detection Method Based on Local Evaluation and Global Optimization
  • Gaze Detection Method Based on Local Evaluation and Global Optimization
  • Gaze Detection Method Based on Local Evaluation and Global Optimization

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Embodiment Construction

[0034] The specific implementation manners of the present invention will be further described below in conjunction with the accompanying drawings and technical solutions.

[0035] Step 1: The candidate frames in the present invention are obtained by the edge box algorithm, which generates 10,000 candidate frames by default. We observed that keeping more candidate boxes can ensure that the candidate set contains all the gaze regions as much as possible, while keeping fewer candidate boxes can increase the efficiency of the algorithm. In order to maintain a balance between accuracy and efficiency, we set the edge box parameter α to 0.65, β to 0.55, and retain 2000 candidate boxes for each picture.

[0036] Step 2: As mentioned above, the convolutional neural network we use is a vgg-16 structure, and the initial parameters are also the parameters of the vgg-16 image classification network. In the process of fine-tuning network parameters, we use the salicon database as training ...

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Abstract

The invention belongs to the field of computer vision and is a fixation point detection method based on local evaluation and global optimization. Use the edge density algorithm to extract possible candidate targets in the image; then use the method of supervised learning to locally evaluate these targets, two evaluation methods: (1) use the images of the entire database to train SVM to score the significance of each proposal; (2) ) use the semi-coupled dictionary learning algorithm to reconstruct different SVMs for different images, and score the proposals of this image in a targeted manner; after local evaluation, the proposals are clustered using the proposal subset optimization algorithm. Finally, global optimization is performed. The present invention designs a model capable of capturing the information of different characteristics that attract human eyes, and can effectively detect the gaze area of ​​human eyes in images containing semantic information, images containing objects, and images that are complex or do not contain objects.

Description

technical field [0001] The invention belongs to the field of computer vision, relates to relevant knowledge of image processing, and in particular relates to a gaze point detection method. Background technique [0002] As a branch of saliency detection, gaze point detection has broad application prospects in image segmentation, image compression, object recognition and other fields. In recent years, many novel algorithms have emerged in the field of eye movement point detection, and the detection effect on some databases has reached a good level, but there are still many key problems that have not been resolved. The classic algorithm of eye movement point detection is analyzed below, and the current development status is summarized. [0003] Itti et al. proposed the earliest fixation point prediction algorithm in the paper "A model of saliency-based visual attention for rapidscene analysis, 1998". They extract the color, brightness, and direction of the image, and use the ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/161G06V40/168G06F18/23G06F18/2411
Inventor 李建华姜博卢湖川
Owner DALIAN UNIV OF TECH
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