Browse > Article
http://dx.doi.org/10.22156/CS4SMB.2022.12.01.015

A Method of Describing and Retrieving Movement of an Object by Using the Shape Variation of an Object  

Choi, Minseok (Division of AI Informatics, Sahmyook University)
Publication Information
Journal of Convergence for Information Technology / v.12, no.1, 2022 , pp. 15-21 More about this Journal
Abstract
In the content-based video retrieval applications, the information on the movement of an object can be used as important in classifying the content. In particular, analyzing and classifying human movement can be used for various purposes as well as retrieval. In this paper, a method to improve the performance of the shape variation descriptor and shape sequence to describe and classify movement using shape information that changes according to the movement of an object is proposed. By selecting a shape descriptor to more efficiently describe the shape information of an object and comparing the distance function used to measure the similarity, the description and retrieval efficiency of movement information can be increased. Through experiments, it was shown that the proposed method can describe movement information more efficiently and increase the retrieval efficiency compared to the previous method.
Keywords
Movement retrieval; Shape variation; Shape descriptor; Shape sequence; Content-based retrieval;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 S. J. Kim. (2015). The present and prospect of Online Video, Music service and Media Usage. Journal of Digital Contents Society, 16(1), 137-144. DOI : 10.9728/dcs.2015.16.1.137   DOI
2 Y. P. Tan, D. D. Saur, S. R. Kulkami & P. J Ramadge. (2000). Rapid estimation of camera motion from compressed video with application to video annotation. IEEE Transactions on Circuits and Systems for Video Technology, 10(1), 133-146. DOI : 10.1109/76.825867   DOI
3 K. M. Lee. (2013). Dynamic Gesture Recognition using SVM and its Application to an Interactive Storybook. Journal of the Korea Contents Association, 13(4), 64-72. DOI : 10.5392/JKCA.2013.13.04.064   DOI
4 J. Y. Lee & J. S. Kwon. (2020). Application of motion recognition technology for interactive implementation in space. Journal of Digital Contents Society, 21(6), 1171-1179. DOI : 10.9728/dcs.2020.21.6.1171   DOI
5 A. F. Bobick & J. W. Davis. (2001). The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(3), 257-267. DOI : 10.1109/34.910878   DOI
6 M. S. Choi & W. Y. Kim. (2004). The description and retrieval of a sequence of moving objects using a shape variation map. Pattern Recognition Letters, 25(12), 1369-1375. DOI : 10.1016/j.patrec.2004.05.010   DOI
7 P. Huang, A. Hilton & J. Starck. (2010). Shape Similarity for 3D Video Sequences of People. International Journal of Computer Vision, 89, 362-381. DOI: 10.1007/s11263-010-0319-9   DOI
8 J. M. Lee & W. Y. Kim. (2012). A New Shape Description Method Using Angular Radial Transform. IEICE Transactions on Information and Systems, E95-D(6), 1628-1635. DOI : 10.1587/transinf.E95.D.1628   DOI
9 I. H. Kim & I. H. Jung. (2021). A Study on Korea Sign Language Motion Recognition Using OpenPose Based on Deep Learning. Journal of Digital Contents Society, 22(4), 681-687. DOI : 10.9728/dcs.2021.22.4.681   DOI
10 H. Choi, H. Park & K. Lee. (2016). Gesture-Based User Authentication using Feature Combination and Block-Wise Correlation Coefficients. Journal of Korean Institute of Next Generation Computing, 12(3), 85-93.
11 S. Jeong. (2011). Gait Recognition Using Shape Sequence Descriptor. Journal of the Korea Academia-Industrial cooperation Society, 12(5), 2339-2345. DOI : 10.5762/KAIS.2011.12.5.2339   DOI
12 M. G. Song, S. J. Jeong, S. H. Choi & K. M. Lee. (2018). Celebrity-indexed video retrieval application using face recognition and tracking. Journal of Digital Contents Society, 19(11), 2049-2058. DOI : 10.9728/dcs.2018.19.11.2049   DOI
13 M. S. Choi. (2009). Movement Search in Video Stream Using Shape Sequence. Journal of Korea Multimedia Society, 12(4), 492-501.
14 S. Lee, Y. S. Choi, W. Lim, T. K. Kwon & H. K. Kim. (2002). Shape-sequence-based key image generation algorithm for browsing and retrieval of video clips. IEE Electronics Letters, 38(12), 549-550. DOI : 10.1049/el:20020382   DOI
15 M. S Choi. (2008). Efficient Representation and Matching of Object Movement using Shape Sequence Descriptor. KIPS Transaction on Software and Data Engineering, 15(5), 391-396. DOI : 10.3745/KIPSTB.2008.15-B.5.391   DOI
16 B. S. Manjunath, J. R. Ohm, V. V. Vasudevan & A. Yamada. (2001). Color and Texture Descriptors. IEEE Transactions on Circuits and Systems for Video Technology, 11(6), 703-715. DOI : 10.1109/76.927424   DOI
17 J. K. Aggarwal & Q. Cai. (1999). Human Motion Analysis: A Review. Computer Vision and Image Understanding, 73(3), 428-440. DOI : 10.1006/cviu.1998.0744   DOI
18 S. Li, M. C. Lee & C. M. Pun. (2009). Complex Zernike Moments Features for Shape-Based Image Retrieval. IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans, 39(1), 227-237. DOI : 10.1109/TSMCA.2008.2007988   DOI