- 期刊
Human Action Analysis Using Motion History Polar Histogram
並列摘要
The human behaviors are frequently analyzed by using the uncompressed video data (raw data). However, the amount of uncompressed video data is too huge to transmit via the network with limited bandwidth. Hence, the rare behavior detection within the compressed video may not only extract the visual features directly from MPEG compressed video but also overcome the limited network bandwidth problem. In this paper, several visual features extracted from the compressed video, e.g., the motion vectors and color feature are applied to develop new action feature descriptor. Based on the action feature descriptor the human actions are detected and then the rare behaviors may be identified. The proposed human action analysis system consists of following novel technologies: (1) Partial decoding of the compressed video stream and extracting of the motion vectors from the p-frame in each GOP, (2) Generation of the object-based accumulative motion vector (OAMV) from continuous three GOPs, (3) Construction of Motion History Polar Histogram (MHPH), and (4) Utilizing of Adaboost algorithm to recognize various kinds of human actions. Experimental results show that the recognition rate can approach 90%.
延伸閱讀
- Liu, H., Wang, L., & Sun, F. (2014). Mean-Shift Tracking Using Fuzzy Coding Histogram. International Journal of Fuzzy Systems, 16(4), 457-467. https://www.airitilibrary.com/Article/Detail?DocID=15622479-201412-201501130006-201501130006-457-467
- Huang, Y. H. (2011). Human Actions Recognition Based on a New Approach: Weighting Vectors of Key Postures and Motion Trajectory [master's thesis, National Taiwan University]. Airiti Library. https://doi.org/10.6342/NTU.2011.00914
- 吳居穆（2007）。Model based human motion recognition using transition diagram〔碩士論文，國立中央大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0031-0207200917343316
- Hu, C. K. (1994). Histogram Monte Carlo Approach to Scaling Functions and Order Parameters of Phase Transition Models. Chinese Journal of Physics, 32(5S), 71-85. https://www.airitilibrary.com/Article/Detail?DocID=05779073-199410-201212180016-201212180016-71-85
- Xie, W., Zhang, Z., Wang, Y., Zhang, Y., & Zhu, L. (2020). The New Fast Point Feature Histograms Algorithm Based on Adaptive Selection. 淡江理工學刊, 23(2), 225-232. https://doi.org/10.6180/jase.202006_23(2).0006