[1]李龙,尹辉,许宏丽,等.一种鲁棒的Multi-Egocentric视频中的多目标检测及匹配算法[J].智能系统学报,2016,11(5):619-626.[doi:10.11992/tis.201603050]
LI Long,Yin Hui,XU Hongli,et al.A robust multi-object detection and matching algorithmfor multi-egocentric videos[J].CAAI Transactions on Intelligent Systems,2016,11(5):619-626.[doi:10.11992/tis.201603050]
点击复制
LI Long,Yin Hui,XU Hongli,et al.A robust multi-object detection and matching algorithmfor multi-egocentric videos[J].CAAI Transactions on Intelligent Systems,2016,11(5):619-626.[doi:10.11992/tis.201603050]
一种鲁棒的Multi-Egocentric视频中的多目标检测及匹配算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第5期
页码:
619-626
栏目:
学术论文—机器学习
出版日期:
2016-11-01
- Title:
- A robust multi-object detection and matching algorithmfor multi-egocentric videos
- 关键词:
- Multi-Egocentric视频; 多目标检测; 多目标匹配
- 分类号:
- TP391.4
- 摘要:
- 针对视频中的背景变化剧烈、目标尺度差异明显和视角时变性强的特点，提出一种鲁棒的针对multi-egocentric视频的多目标检测及匹配算法。首先，构建基于boosting方法的多目标检测模型对各视频序列中的显著目标进行粗检测，并提出一种基于局部相似度的区域优化算法对粗检测显著目标的轮廓进行优化，提高Egocentric视频中显著目标轮廓检测和定位的准确性。在显著目标检测基础上，对不同视角中的显著目标构建基于HOG特征的SVM分类器，实现多视角的多目标匹配。在Party Scene数据集上的实验验证了本文算法的有效性。
- Abstract:
- In this paper, a robust multi-object detection and matching algorithm for a multi-egocentric video is proposed by considering the characteristics of multi-egocentric videos, for example, sudden changes in background, and variable target scales and viewpoints. First, a multi-target detection model based on a boosting method is constructed, to roughly detect any salient objects in the video frames. Then an optimization algorithm based on local similarity is proposed for optimizing the salient-object area and improving the accuracy of salient-object detection and localization. Finally, a SVM classifier based on HOG features is trained to realize multi-target matching in multi-egocentric videos. Experiments using Scene Party datasets show the effectiveness of the proposed method.
- 参考文献/References:
-
[1] FELZENSZWALB P, MCALLESTER D, RAMANAN D. A discriminatively trained, multiscale, deformable part model[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK:IEEE, 2008:1-8.
[2] BOURDEV L, MALIK J. Poselets:body part detectors trained using 3d human pose annotations[C]//Proceedings of the 2009 IEEE 12th International Conference on Computer Vision. Kyoto:IEEE, 2009:1365-1372.
[3] WEINZAEPFEL P, REVAUD J, HARCHAOUI Z, et al. Learning to detect motion boundaries[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA:IEEE, 2015:2578-2586.
[4] KUMAR K S, PRASAD S, SAROJ P K, et al. Multiple cameras using real time object tracking for surveillance and security system[C]//Proceedings of the 20103rd International Conference on Emerging Trends in Engineering and Technology. Goa:IEEE, 2010:213-218.
[5] SOO PARK H, SHI Jianbo. Social saliency prediction[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA:IEEE, 2015:4777-4785.
[6] LIN Yuewei, ABDELFATAH K, ZHOU Youjie, et al. Co-interest person detection from multiple wearable camera videos[C]//Proceedings of the IEEE International Conference on Computer Vision. Santiago:IEEE, 2015:4426-4434.
[7] SNAVELY N, SEITZ S M, SZELISKI R. Photo tourism:exploring photo collections in 3D[J]. ACM transactions on graphics (TOG), 2006, 25(3):835-846.
[8] YONETANI R, KITANI K M, SATO Y. Ego-surfing first person videos[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA:IEEE, 2015:5445-5454.
[9] FREUND Y, SCHAPIRE R. A short introduction to boosting[J]. Journal of Japanese society for artificial intelligence, 1999, 14(5):771-780.
[10] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[M]//FLEET D, PAJDLA T, SCHIELE B, et al. Computer Vision-ECCV 2014. Switzerland:Springer International Publishing, 2014:346-361.
[11] LAZEBNIK S, SCHMID C, PONCE J. Beyond bags of features:spatial pyramid matching for recognizing natural scene categories[C]//Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, NY, USA:IEEE, 2006, 2:2169-2178.
[12] BURGES C J C. A tutorial on support vector machines for pattern recognition[J]. Data mining and knowledge discovery, 1998, 2(2):121-167.
[13] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA:IEEE, 2005, 1:886-893.
[14] ZHU Wangjiang, LIANG Shuang, WEI Yichen, et al. Saliency optimization from robust background detection[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH:IEEE, 2014:2814-2821.
- 相似文献/References:
-
[1]欧伟奇,尹辉,许宏丽,等.一种基于Multi-Egocentric视频运动轨迹重建的多目标跟踪算法[J].智能系统学报,2019,14(2):246.[doi:10.11992/tis.201709003]
OU Weiqi,YIN Hui,XU Hongli,et al.A multi-object tracking algorithm based on trajectory reconstruction on multi-egocentric video[J].CAAI Transactions on Intelligent Systems,2019,14():246.[doi:10.11992/tis.201709003]
备注/Memo
收稿日期:2016-03-20。
基金项目:国家自然科学基金项目（61472029，61473031）.
作者简介:李龙,男,1982年生,硕士研究生,主要研究方向为图像处理与计算机视觉;尹辉,女,1972年生,副教授,博士生导师,主要研究方向为计算机视觉、模式识别以及神经计算;许宏丽,女,1963年生,教授,主要研究方向为计算机技术、机器学习以及认知计算。
通讯作者:李龙.E-mail:hyin@djpu.edu.cn
更新日期/Last Update:
1900-01-01