基于空時(shí)多線索融合的超像素運(yùn)動目標(biāo)檢測方法

宋濤; 李鷗; 劉廣怡

doi:10.11999/JEIT150950

基于空時(shí)多線索融合的超像素運(yùn)動目標(biāo)檢測方法

doi: 10.11999/JEIT150950

基金項(xiàng)目:

國家科技重大專項(xiàng)(2014ZX03006003)

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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-08-19
- 修回日期: 2015-12-13
- 刊出日期: 2016-06-19

Moving Object Detection Method Via Superpixels Based on Spatiotemporal Multi-cues Fusion

Funds:

National Science and Technology Major Projects of China (2014ZX03006003)

摘要

摘要: 運(yùn)動目標(biāo)檢測是計(jì)算機(jī)視覺領(lǐng)域極具挑戰(zhàn)性的難題，該文針對這一問題提出一種基于空時(shí)多線索融合的超像素運(yùn)動目標(biāo)檢測方法。首先利用簡單線性迭代聚類算法將當(dāng)前幀分割為超像素集合，根據(jù)幀間的像素級時(shí)變線索找到當(dāng)前幀中包含運(yùn)動信息的前景超像素子塊；然后根據(jù)運(yùn)動目標(biāo)的一致性原則建立前一幀目標(biāo)模型，結(jié)合目標(biāo)空間線索進(jìn)一步確定包含運(yùn)動目標(biāo)的檢測窗口，將目標(biāo)檢測問題轉(zhuǎn)化為目標(biāo)分割問題，利用密集角點(diǎn)檢測將目標(biāo)從窗口中分割出來。在多個(gè)具有挑戰(zhàn)性的公開視頻序列上同幾種流行檢測算法的實(shí)驗(yàn)對比結(jié)果證明了所提算法的有效性和優(yōu)越性。
- 運(yùn)動目標(biāo)檢測 /
- 超像素分割 /
- 空時(shí)多線索 /
- 前景目標(biāo)模型 /
- 目標(biāo)分割
Abstract: Moving object detection is a challenging issue in computer vision. In this paper, a new detection method via superpixels is proposed based on spatiotemporal multi-cues fusion. First, the current frame is segmented into a set of superpixels using simple linear iterative clustering and the subblocks of foreground superpixels containing motion information are captured according to the time-varying cue of inter-frame pixel-level. Then, a target model of the previous frame, which is established on the basis of the consistency principle of motion target and space clues of a target, are combined to further determine the detection window including the moving object. Finally, the problem of object detection is converted to object segmentation and an object is divided from the detection window utilizing the dense corner detection. Experimental results using several challenging public video sequences show the effectiveness and superiority of the proposed method compared with other state-of-the-art detection approaches.
- Moving object detection /
- Superpixel segmentation /
- Spatiotemporal multi-cues /
- Foreground target model /
- Object segmentation

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