Improvement of accuracy for human action recognition by histogram of changing points and average speed descriptors

被引：1
作者：
Vu, Thi Ly [1 ]
Do, Trung Dung [1 ]
Jin, Cheng-Bin [1 ]
Li, Shengzhe [1 ]
Nguyen, Van Huan [1 ]
Kim, Hakil [1 ]
Lee, Chongho [1 ]
机构：
[1] School of Information and Communication Engineering, Inha University, Incheon
来源：
Journal of Computing Science and Engineering | 2015年 / 9卷 / 01期
关键词：
AS; Dense sampling; HCP; HOF; HOG; Human action recognition; SVM;
D O I：
10.5626/JCSE.2015.9.1.29
中图分类号：
X9 [安全科学];
学科分类号：
0837 ;
摘要：
Human action recognition has become an important research topic in computer vision area recently due to many applications in the real world, such as video surveillance, video retrieval, video analysis, and human-computer interaction. The goal of this paper is to evaluate descriptors which have recently been used in action recognition, namely Histogram of Oriented Gradient (HOG) and Histogram of Optical Flow (HOF). This paper also proposes new descriptors to represent the change of points within each part of a human body, caused by actions named as Histogram of Changing Points (HCP) and so-called Average Speed (AS) which measures the average speed of actions. The descriptors are combined to build a strong descriptor to represent human actions by modeling the information about appearance, local motion, and changes on each part of the body, as well as motion speed. The effectiveness of these new descriptors is evaluated in the experiments on KTH and Hollywood datasets. © 2015.
引用
收藏
页码：29 / 38
页数：9
相关论文
共 27 条
  • [1] Wang H., Klaser A., Schmid C., Liu C., Dense trajectories and motion boundary descriptors for action recognition, International Journal of Computer Vision, 103, 1, pp. 60-79, (2013)
  • [2] Nanni L., Brahnam S., Lumini A., Local ternary patterns from three orthogonal planes for human action classification, Expert Systems with Applications, 38, 5, pp. 5125-5128, (2011)
  • [3] Mattivi R., Shao L., Human action recognition using LBP-TOP as sparse spatio-temporal feature descriptor, Proceedings of the 13th International Conference on Computer Analysis Images and Patterns (CAIP), pp. 740-747, (2009)
  • [4] Laptev I., Marszalek M., Schmid C., Rozenfeld B., Learning realistic human actions from movies, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-8, (2008)
  • [5] Jain M., Jegou H., Bouthemy P., Better exploiting motion for better action recognition, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2555-2562, (2013)
  • [6] Shabani A.H., Clausi D.A., Zelek J.S., Improved Spatio-temporal Salient feature detection for action recognition, Proceedings of British Machine Vision Conference (BMVC), pp. 1-12, (2011)
  • [7] Laptev I., On space-time interest points, International Journal of Computer Vision, 64, 2-3, pp. 107-123, (2005)
  • [8] Dollar P., Rabaud V., Cottrell G., Belongie S., Behavior recognition via sparse spatio-temporal features, Proceedings of the 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), pp. 65-72, (2005)
  • [9] Jhuang H., Serre T., Wolf L., Poggio T., A biologically inspired system for action recognition, Proceedings of IEEE 11th International Conference on Computer Vision (ICCV), pp. 1-8, (2007)
  • [10] Wong K.Y.K., Cipolla R., Extracting spatio-temporal interest points using global information, Proceedings of IEEE 11th International Conference on Computer Vision (ICCV), pp. 1-8, (2007)
123