Video significance detection method and system based on time-space constraints

A detection method, a remarkable technology, applied in the field of video, can solve the problem of insufficient robustness, etc.

Active Publication Date: 2017-11-24
SHENZHEN UNIV
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a video saliency detection method and system based on spati

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  • Video significance detection method and system based on time-space constraints
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  • Video significance detection method and system based on time-space constraints

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[0074] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0075] figure 1 It shows a video saliency detection method based on spatio-temporal constraints provided by an embodiment of the present invention, including:

[0076] S101. Perform superpixel segmentation on a current frame to be detected of a video to be detected to obtain a current frame and a superpixel set after superpixel segmentation.

[0077] In this step, a simple linear iterative clustering algorithm (SLIC, Simple Linear Iterative Clustering) may be used for superpixel segmentation of the current frame to be detected, but is not limited to this method. Superpixel segmentation is a prepr...

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Abstract

The invention is applied to the field of video detection, and provides a video significance detection method. The method comprises the steps that superpixel segmentation is conducted on a to-be-detected current frame to obtain a current frame after superpixel segmentation, optical flow field motion estimation is calculated according the current frame and a previous frame, motion distribution energy and motion edge energy are calculated, motion history energy is calculated according to the current frame and the previous frame, and a hybrid motion energy diagram is generated according to the above-mentioned features and a significance diagram of the previous frame; and an initial target segmentation area of the hybrid motion energy diagram is obtained, a reliable target area and a reliable background area are extracted, and a significance global optimization model is constructed according to the reliable target area, the reliable background area and the hybrid motion energy diagram and then solved to obtain a significance diagram of the current frame. According to the video significance detection method, by adopting multiple motion features and space features such as the motion distribution energy of an area layer, the motion edge energy of an edge layer, the motion history energy of a pixel layer and the significance diagram of the previous frame, and the robustness and stability of significance detection are enhanced.

Description

technical field [0001] The invention belongs to the field of video technology, and in particular relates to a video saliency detection method and system based on time-space constraints. Background technique [0002] Saliency detection aims to predict relatively noticeable areas visually. It has a wide range of applications in video classification, video retrieval, video summarization, scene understanding, object tracking and other fields. It is the foundation and key issue of computer vision. Since motion information is an important clue for video saliency detection, unlike static image saliency detection which only considers spatial information, video saliency detection should consider both motion information and spatial information. [0003] How to extract the motion information of salient objects is a key issue in video saliency detection. At present, most methods use the optical flow field to estimate the motion of the salient target, but the optical flow field is very ...

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Application Information

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IPC IPC(8): G06T7/11G06T7/13G06T7/194G06T7/207
CPCG06T2207/10016G06T2207/20036G06T7/11G06T7/13G06T7/194G06T7/207
Inventor 邹文斌陈宇环王振楠李霞徐晨
Owner SHENZHEN UNIV
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