Human Trajectory Analysis Method Based on Hidden Markov Model in Home Intelligent Space
ZHAO Yang1, TIAN Guo-Hui1, YIN Jian-Qin2, FAN Jian-Xia1
1.School of Control Science and Engineering, Shandong University, Jinan 250061 2.School of Information Science and Engineering, University of Jinan, Jinan 250022
Abstract Recognition and pre-reasoning of human activity is essential in home intelligent space. In this paper, an approach based on hidden markov model (HMM) is proposed for human trajectory analysis. Firstly, the plan is discretized into tile blocks, and HMM models for human trajectories are set up off-line. Then, aiming at the online analysis, a sliding-window-like approach is put forward to achieve real-time trajectory segmentation and activate model matching process intelligently. Finally, a predicition of human trajectory is made by the intelligent space according to matching results. Experimental results show that the proposed approach achieves good performance in real-time trajectory analysis. Furthermore, the proposed approach can help home intelligent space make wiser decision.
ZHAO Yang,TIAN Guo-Hui,YIN Jian-Qin等. Human Trajectory Analysis Method Based on Hidden Markov Model in Home Intelligent Space[J]. , 2015, 28(6): 542-549.
[1] Morris B T, Trivedi M M. A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance. IEEE Trans on Circuits and Systems for Video Technology, 2008, 18(8): 1114-1127 [2] Morris B T, Trivedi M M. Learning Trajectory Patterns by Clustering: Experimental Studies and Comparative Evaluation // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA, 2009: 312-319 [3] Morris B T, Trivedi M M. Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive App-roach. IEEE Trans on Pattern Analysis and Machine Intelligence, 2011, 33(11): 2287-2301 [4] Beh J, Han D K, Durasiwami R, et al. Hidden Markov Model on a Unit Hypersphere Space for Gesture Trajectory Recognition. Pattern Recognition Letters, 2014, 36: 144-153 [5] Li H J, Yi Y, Li X X, et al. Human Activity Recognition Based on HMM by Improved PSO and Event Probability Sequence. Journal of Systems Engineering and Electronics, 2013, 24(3): 545-554 [6] Houenou A, Bonnifait P, Cherfaoui V, et al. Vehicle Trajectory Prediction Based on Motion Model and Maneuver Recognition // Proc of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Tokyo, Japan, 2013: 4363-4369 [7] Antonakaki P, Kosmopoulos D, Perantonis S J. Detecting Abnormal Human Behaviour Using Multiple Cameras. Signal Processing, 2009, 89(9): 1723-1738 [8] Jiang M, Chen Y Y, Zhao Y Y, et al. A Real-Time Fall Detection System Based on HMM and RVM // Proc of the IEEE International Conference on Visual Communications and Image Processing. Kuching, Malaysia, 2013. DOI: 10.1109/VCIP.2013.6706385 [9] Yan Y Z. Target Position and Trajectory Analysis Based on Distributed Vision in Home Environment. Master Dissertation. Jinan, China: Shandong University, 2013 (in Chinese) (闫云章.家庭环境下基于分布式视觉的目标定位及轨迹分析.硕士学位论文.济南:山东大学, 2013) [10] Panagiotakis C, Pelekis N, Kopanakis I, et al. Segmentation and Sampling of Moving Object Trajectories Based on Representativeness. IEEE Trans on Knowledge and Data Engineering, 2012, 24(7): 1328-1343 [11] Tian G H, Li X L, Zhao S P, et al. Research and Development of Intelligent Space Technology for a Home Service Robot. Journal of Shandong University: Engineering Science, 2007, 37(5): 53-59 (in Chinese) (田国会,李晓磊,赵守鹏,等.家庭服务机器人智能空间技术研究与进展.山东大学学报:工学版, 2007, 37(5): 53-59) [12] Yin J Q, Tian G H, Jiang H T, et al. Human Action Recognition Oriented to Family Service. Journal of Sichuan University: Engineering Science Edition, 2011, 43(4): 101-107 (in Chinese) (尹建芹,田国会,姜海涛,等.面向家庭服务的人体动作识别.四川大学学报:工程科学版, 2011, 43(4): 101-107) [13] Zhou S. Generating Natural Language Description for Vehicle Tra-jectories Based on HMM. Master Dissertation. Beijing, China: Beijing University of Posts and Telecommunications, 2012 (in Chinese) (周 珊.基于 HMM 的行车轨迹自然语言描述生成研究.硕士学位论文.北京:北京邮电大学, 2012) [14] Tong L N, Song Q J, Ge Y J, et al. HMM-Based Human Fall De- tection and Prediction Method Using Tri-axial Accelerometer. IEEE Sensors Journal, 2013, 13(5): 1849-1856 [15] Li K, Fu Y. ARMA-HMM: A New Approach for Early Recognition of Human Activity // Proc of the 21st International Conference on Pattern Recognition. Tsukuba, Japan, 2012: 1779-1782 [16] Nascimento J C, Figueiredo M A T, Marques J S. Trajectory Cla-ssification Using Switched Dynamical Hidden Markov Models. IEEE Trans on Image Processing, 2009, 19(5): 1338-1348 [17] Yu T, Zou J H. A Method of Combining Bayes Rule with HMM in Gait Recognition. Chinese Journal of Computers, 2012, 35(2): 386-396 (in Chinese) (余 涛,邹建华.基于Bayes规则与HMM相结合的步态识别方法研究.计算机学报, 2012, 35(2): 386-396)
2015, Vol. 28 
