基于空时多线索融合的超像素运动目标检测方法

宋涛; 李鸥; 刘广怡

doi:10.11999/JEIT150950

基于空时多线索融合的超像素运动目标检测方法

doi: 10.11999/JEIT150950

基金项目:

国家科技重大专项(2014ZX03006003)

计量
- 文章访问数: 1511
- HTML全文浏览量: 114
- PDF下载量: 774
- 被引次数: 0
出版历程
- 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: 运动目标检测是计算机视觉领域极具挑战性的难题，该文针对这一问题提出一种基于空时多线索融合的超像素运动目标检测方法。首先利用简单线性迭代聚类算法将当前帧分割为超像素集合，根据帧间的像素级时变线索找到当前帧中包含运动信息的前景超像素子块；然后根据运动目标的一致性原则建立前一帧目标模型，结合目标空间线索进一步确定包含运动目标的检测窗口，将目标检测问题转化为目标分割问题，利用密集角点检测将目标从窗口中分割出来。在多个具有挑战性的公开视频序列上同几种流行检测算法的实验对比结果证明了所提算法的有效性和优越性。
- 运动目标检测 /
- 超像素分割 /
- 空时多线索 /
- 前景目标模型 /
- 目标分割
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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参考文献(24)

HU W, TAN T, and WANG L. A survey on visual surveillance of object motion and behaviors[J]. IEEE Transactions on Systems, Man and Cybernetics, 2004, 34(3): 334-352. doi: 10.1109/TSMCC.2004.829274.

BROX T and MALIK J. Large displacement optical flow: descriptor matching in variational motion estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(3): 500-513. doi: 10.1109/TPAMI.2010. 143.

RADKE R J, ANDRA S, and Al-KOFAHI O. Image change detection algorithms: a systematic survey[J]. IEEE Transactions on Image Processing, 2005, 14(3): 294-307. doi: 10.1109/TIP.2004.838698.

周建英, 吴小培, 张超, 等. 基于滑动窗的混合高斯模型运动目标检测方法[J]. 电子与信息学报, 2013, 35(7): 1650-1656. doi: 10.3724/SP.J.1146.2012.01449.

ZHOU Jianying, WU Xiaopei, ZHANG Chao, et al. A moving object detection method based on sliding window Gaussian mixture model[J]. Journal of Electronics Information Technology, 2013, 35(7): 1650-1656. doi: 10.3724/SP.J.1146. 2012.01449.

VAN D M and BARNICH O. ViBe: a disruptive method for background subtraction[C]. Proceedings of the Background Modeling and Foreground Detection for Video Surveillance, CRC, USA, 2014: 1-23.

ST-CHARLES P L and BILODEAU G A. Improving background subtraction using local binary similarity patterns[C]. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, 2014: 509-515.

CHEN Shengyong, ZHANG Jianhua, and LI Youfu. A hierarchical model incorporating segmented regions and pixel descriptors for video background subtraction[J]. IEEE Transactions on Industrial Informatics, 2012, 8(1): 118-127. doi: 10.1109/TII.2011.2173202.

STAUFFER C and GRIMSON W. Adaptive background mixture models for real-time tracking[C]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, 1999: 246-252.

EVANGELIO R H, PATZOLD M, and KELLER I. Adaptively splitted GMM with feedback improvement for the task of background subtraction[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(5): 863-874. doi: 10.1109/TIFS.2014.2313919.

MARTINS P, CASEIRO R, and BATISTA J. Non- parametric Bayesian constrained local models[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014: 1797-1804.

BARNICH O and VAN D M. ViBe: a universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724. doi: 10.1109/TIP.2010.2101613.

庄哲民, 章聪友, 杨金耀, 等. 基于灰度特征和自适应阈值的虚拟背景提取研究[J]. 电子与信息学报, 2015, 37(2): 346-352. doi: 10.11999/JEIT140317.

ZHUANG Zhemin, ZHANG Congyou, YANG Jinyao, et al. Investigation on visual background extractor based on gray feature and adaptive threshold[J]. Journal of Electronics Information Technology, 2015, 37(2): 346-352. doi: 10.11999/ JEIT140317.

ST-CHARLES P, BILODEAU G, and BERGEVIN R. SuBSENSE: a universal change detection method with local adaptive sensitivity[J]. IEEE Transactions on Image Processing, 2015, 24(1): 359-373. doi: 10.1109/TIP.2014. 2378053.

MOGHADAM A A, KUMAR M, and RADHA H. Common and Innovative visuals: a sparsity modeling framework for video[J]. IEEE Transactions on Image Processing, 2014, 23(9): 4055-4069. doi: 10.1109/TIP.2014.2321476.

ALEXE B, DESELAERS T, and FERRARI V. Measuring the objectness of image windows[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2189-2202. doi: 10.1109/TPAMI.2012.28.

ZHANG Luming, XIA Yingjie, JI Rangping, et al. Spatial-aware object-level saliency prediction by learning graphlet hierarchies[J]. IEEE Transactions on Industrial Electronics, 2015, 62(2): 1301-1308. doi: 10.1109/TIE.2014. 2336602.

LIU Zhi, ZOU Wenbin, and MEUR O L. Saliency tree: a novel saliency detection framework[J]. IEEE Transactions on Image Processing, 2014, 23(5): 1937-1952. doi: 10.1109/TIP. 2014.2307434.

XU Li, JIA Jiaya, and MATSUSHITA Y. Motion detail preserving optical flow estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(9): 1744-1757. doi: 10.1109/TPAMI.2011.236.

LIU Zhi, ZHANG Xiang, LUO Shuhua, et al. Superpixel-based spatiotemporal saliency detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(9): 1522-1540. doi: 10.1109/TCSVT.2014.2308642.

WU Jianxin and REHG J M. CENTRIST: a visual descriptor for scene categorization[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1489-1501. doi: 10.1109/TPAMI.2010.224.

ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2281. doi: 10.1109/TPAMI.2012.120.

WANG Yi, JODOIN P M, and PORIKLI F. CDnet 2014: an expanded change detection benchmark dataset[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, OH, 2014: 393-400.

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