基于多線索的運動手部分割方法

阮曉鋼; 林佳; 于乃功; 朱曉慶; OuattaraSie

doi:10.11999/JEIT160730

基于多線索的運動手部分割方法

doi: 10.11999/JEIT160730 cstr: 32379.14.JEIT160730

基金項目:

國家自然科學(xué)基金(61375086)，北京市教育委員會科技計劃重點項目(KZ201610005010)

計量
- 文章訪問數(shù): 1098
- HTML全文瀏覽量: 125
- PDF下載量: 372
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-07-08
- 修回日期: 2017-01-03
- 刊出日期: 2017-05-19

Moving Hand Segmentation Based on Multi-cues

Funds:

The National Natural Science Foundation of China (61375086), The Key Project of ST Plan of Beijing Municipal Commission of Education (KZ201610005010)

摘要

摘要: 分割運動手部時，為了不依賴不合理的假設(shè)和解決手臉遮擋問題，該文提出一種基于膚色、灰度、深度和運動線索的分割方法。首先，利用灰度與深度光流的方差信息來自適應(yīng)提取運動感興趣區(qū)域(Motion Region of Interest, MRoI)，以定位人體運動部位。然后，在MRoI中檢測滿足膚色與自適應(yīng)運動約束的角點作為皮膚種子點。接著，根據(jù)膚色、深度與運動準(zhǔn)則將皮膚種子點生長為候選手部區(qū)域。最后，通過邊緣深度梯度、骨架提取和最優(yōu)路徑搜索從候選手部區(qū)域中分割出運動手部區(qū)域。實驗結(jié)果表明，在不同情形下，特別是手臉遮擋時，該方法可以有效和準(zhǔn)確地分割出運動手部區(qū)域。
- 運動手部分割 /
- 多線索 /
- 不依賴假設(shè) /
- 手臉遮擋
Abstract: For moving hand segmentation, in order not to use unreasonable assumptions and to solve the hand-face occlusion, a segmentation method based on skin color, grayscale, depth and motion cues is proposed. Firstly, according to the variance information of grayscale and depth optical flow, Motion Region of Interest (MRoI) is adaptively extracted to locate the moving body part. Then, corners which satisfy skin color and adaptive motion constraints are detected as skin seed points in the MRoI. Next, skin seed points are grown to [JL1]obtain candidate hand region utilizing skin color, depth and motion criterions. Finally, edge depth gradient, skeleton extraction and optimal path search are employed to segment moving hand region from candidate hand region. Experiment results show that the proposed method can effectively and accurately segment moving hand region under different circumstances, especially when the face is occluded by the hand.
- Moving hand segmentation /
- Multi-cues /
- Assumption free /
- Hand-face occlusion

HTML全文

參考文獻(29)

SHIEH Mingyuan, HSIEH Chungyu, and HSIEH Tsungmin. Fuzzy visual detection for human-robot interaction[J]. Engineering Computations, 2014, 31(8): 1709-1719. doi: 10.1108/EC-11-2012-0292.

張旭東, 楊靜, 胡良梅, 等. 基于多層運動歷史圖像的飛行時間相機人體運動識別[J]. 電子與信息學(xué)報, 2014, 36(5): 1139-1144. doi: 10.3724/SP.J.1146.2013.01003.

ZHANG Xudong, YANG Jing, HU Liangmei, et al. Human activity recognition using Multi-Layered motion history images with Time-Of-Flight camera[J]. Journal of Electronic Information Technology, 2014, 36(5): 1139-1144. doi: 10.3724/SP.J.1146.2013.01003.

WAN Jun, RUAN Qiuqi, LI Wei, et al. One-shot learning gesture recognition from RGB-D Data using Bag of Features [J]. Journal of Machine Learning Research, 2013, 14: 2549-2582.

WAN Jun, GUO Guodong, and LI S Z. Explore efficient local features from RGB-D data for one-shot learning gesture recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(8): 1626-1639. doi: 10.1109/ TPAMI.2015.251347.

HERNANDEZ-VELA A, BAUTISTA M A, PEREZ-SALA X, et al. Probability-based dynamic time warping and bag-of- visual-and-depth -words for human gesture recognition in RGB-D[J]. Pattern Recognition Letters, 2014, 50[JL2]: 112-121. doi: 10.1016/j.patrec.2013.09.009.

MONNIER C, GERMAN S, and OST A. A multi-scale boosted detector for efficient and robust gesture recognition [C]. Proceedings of the European Conference on Computer Vision Workshops, Zurich, 2014: 491-502. doi: 10.1007/978- 3-319-16178-5_34.

PFISTER T, CHARLES J, and ZISSERMAN A. Domain- adaptive discriminative one-shot learning of gestures[C]. Proceedings of the European Conference on Computer Vision, Zurich, 2014: 814-829. doi: 10.1007/978-3-319-10599-4_52.

YANG H D and LEE S W. Simultaneous spotting of signs and fingerspellings based on hierarchical conditional random fields and boostmap embeddings[J]. Pattern Recognition, 2010, 43(8): 2858-2870. doi: 10.1016/ j.patcog.2010.03.007.

FANELLO S R, GORI I, METTA G, et al. Keep it simple and sparse: Real-time action recognition[J]. Journal of Machine Learning Research, 2013, 14: 2617-2640.

LUI Yuiman. Human gesture recognition on product manifolds[J]. Journal of Machine Learning Research, 2012, 13: 3297-3321.

KONECNY J and HAGARA M. One-shot-learning gesture recognition using HOG-HOF Features[J]. Journal of Machine Learning Research, 2014, 15: 2513-2532.

JADOOKI S, MOHAMAD D, TANZILA S, et al. Fused features mining for depth-based hand gesture recognition to classify blind human communication[J]. Neural Computing and Applications, 2016: 1-10. doi: 10.1007/s00521-016- 2244- 5.

DOMINIO F, DONADEO M, and ZANUTTIGH P. Combining multiple depth-based descriptors for hand gesture recognition[J]. Pattern Recognition Letters, 2014, 50: 101-111. doi: 10.1016/j.patrec.2013.10.010.

LIANG Hui, YUAN Junsong, and THALMANN D. 3D fingertip and palm tracking in depth image sequences[C]. Proceedings of the ACM International Conference on Multimedia, Nara, 2012: 785-788. doi: 10.1145/2393347. 2396312.

JIANG Hairong, DUERSTOCK B S, and WACHS J P. A machine vision-based gestural interface for people with upper extremity physical impairments[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014, 44(5): 630-641. doi: 10.1109/TSMC.2013.2270226.

CHENG Hong, DAI Zhongjun, LIU Zicheng, et al. An image-to-class dynamic time warping approach for both 3D static and trajectory hand gesture recognition[J]. Pattern Recognition, 2016, 55: 137-147. doi: 10.1016/j.patcog.2016.01. 011.

WANG Chong, LIU Zhong, and CHAN Shingchow. Superpixel-based hand gesture recognition with Kinect depth camera[J]. IEEE Transactions on Multimedia, 2015, 17(1): 29-39. doi: 10.1109/TMM.2014.2374357.

CHEMYSHOV V and MESTETSKIY L. Real-time hand detection using continuous skeletons[J]. Pattern Recognition and Image Analysis, 2016, 26(2): 368-373. doi: 10.1134/ S1054661816020048.

南棟, 畢篤彥, 查宇飛, 等. 基于參數(shù)估計的無參考型圖像質(zhì)量評價算法[J]. 電子與信息學(xué)報, 2013, 35(9): 2066-2072. doi: 10.3724/SP.J.1146.2012.01652.

NAN Dong, BI Duyan, ZHA Yufei, et al. A no-reference image quality assessment method based on parameter estimation[J]. Journal of Electronic Information Technology, 2013, 35(9): 2066-2072. doi: 10.3724/SP.J.1146. 2012.01652.

JONES M J and REHG J M. Statistical color models with application to skin detection[J]. International Journal of Computer Vision, 2002, 46(1): 81-96. doi: 10.1023/A: 1013200319198.

STERGIOPOULOU E, SGOUROPOULOS K, NIKOLAOU N, et al. Real time hand detection in a complex background [J]. Engineering Applications of Artificial Intelligence, 2014, 35[JL3]: 54-70. doi: 10.1016/j.engappai.2014.06.006.

LIN Jia, RUAN Xiaogang, YU Naigong, et al. One-shot learning gesture recognition based on improved 3D SMoSIFT feature descriptor from RGB-D videos[C]. Proceedings of the Chinese Control and Decision Conference, Qingdao, 2015: 4911-4916. doi: 10.1109/CCDC.2015.7162803.

FARNEBACK G. Two-frame motion estimation based on polynomial expansion[C]. Proceedings of the Scandinavian Conference on Image Analysis, Halmstad, 2003: 363-370. doi: 10.1007/3-540-45103-X_50.

SHI J and TOMASI G. Good feature to track[C]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, 1994: 593-600. doi: 10.1109/CVPR.1994.323794.

GONZALEZ R C and WOODS R E. Digital Image Processing[M]. Beijing: Publishing House of Electronic Industry, 2008: 543-545.

CALABI L and HARNETT W E. Shape recognition, prairie fires, convex deficiencies and skeletons[J]. The American Mathematical Monthly, 1968, 75(4): 335-342. doi: 10.2307/ 2313409.

DIJKSTRA E W. A note on two problems in connexion with graphs[J]. Numerische Mathematik, 1959, 1(1): 269-271. doi: 10.1007/BF01386390.

相關(guān)文章

施引文獻

資源附件(0)

訪問統(tǒng)計