Time-scale Invariant Modeling and Classifying for Object Behaviors in 3D Space Based on Monocular Vision
-
摘要: 提出一种单目视觉下在线识别目标三维行为的方法. 该方法用匹配的标记点估计帧间相似变换,然后转换相似矩阵到对数空间以获取一致的四自由度运动参数序列. 为解决持续时间敏感问题,提出基于多边形近似算法的时间尺度不变特征,并用动态规划实现特征序列的在线提取. 在行为识别阶段,基于动态时间规整训练有限类别行为模板用于匹配测试行为序列. 实验结果表明,该行为模板较对比方法类别可分性平均提高60%以上,并且可用于在线识别连续视频中的未知行为.Abstract: We present an approach to classify 3D behaviors online under monocular vision. We estimate similarity transformation between frames by matched markers, then transforms the similarity matrixes to logarithmic space to generate unified parameter sequence with 4 degrees of freedom. To eliminate the sensitivity of duration time, we formulate a time-scale invariant feature(TSIF) based on polygonal approximation algorithm, and implement online feature picking-up with dynamic programming. In the recognition phase, we use dynamic time warping to train the behavior templates with limited categories then recognize the test sequences. The experimental results show that the class separability of the proposed behavior template is increased by at least 60% to the comparative approaches, furthermore, recognizing unknown behaviors in continuous video online is achieved.
-
[1] Gu Jun-Xia, Ding Xiao-Qing, Wang Sheng-Jin. A survey of activity analysis algorithms. Journal of Image and Graphics, 2009, 14(3): 377-387(谷军霞, 丁晓青, 王生进. 行为分析算法综述. 中国图象图形学报, 2009, 14(3): 377-387) [2] [2] Ji X F, Liu H H. Advances in view-invariant human motion analysis: a review. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2010, 40(1): 13-24 [3] [3] Parameswaran V, Chellappa R. View invariance for human action recognition. International Journal of Computer Vision, 2006, 66(1): 83-101 [4] [4] Weinland D, Ronfard R, Boyer E. Free viewpoint action recognition using motion history volumes. Computer Vision and Image Understanding, 2006, 104(2-3): 249-257 [5] [5] Yamato J, Ohya J, Ishii K. Recognizing human action in time-sequential images using hidden Markov model. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Champaign, IL: IEEE, 1992. 379-385 [6] [6] Brand M, Oliver N, Pentland A. Coupled hidden Markov models for complex action recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Juan: IEEE, 1997. 994-999 [7] [7] Galata A, Johnson N, Hogg D. Learning variable-length Markov models of behavior. Computer Vision and Image Understanding, 2001, 81(3): 398-413 [8] [8] Luo Y, Wu T D, Hwang J N. Object-based analysis and interpretation of human motion in sports video sequences by dynamic Bayesian networks. Computer Vision and image Understanding, 2003, 92(2-3): 196-216 [9] Du You-Tian, Chen Feng, Xu Wen-Li. Approach to human activity multi-scale analysis and recognition based on multi-layer dynamic bayesian network. Acta Automatica Sinica, 2009, 35(3): 225-232(杜友田, 陈峰, 徐文立. 基于多层动态贝叶斯网络的人的行为多尺度分析及识别方法. [10] 自动化学报, 2009, 35(3): 225-232) [11] Bobick A F, Davis J W. The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(3): 257-267 [12] Yilmaz A, Shah M. Actions sketch: a novel action representation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE, 2005. 984-989 [13] Webb J A, Aggarwal J K. Structure from motion of rigid and jointed objects. Artificial Intelligence, 1982, 19(1): 107-130 [14] Perez J C, Vidal E. Optimum polygonal approximation of digitized curves. Pattern Recognition Letters, 1994, 15(8): 743-750 [15] Gavrila D M, Davis L S. 3-D model-based tracking of humans in action: a multi-view approach. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco: IEEE, 1996. 73-80 [16] Bar-Shalom Y. Tracking and Data Association. San Diego, CA: Academic Press Professional, Inc., 1987 [17] Arulampalam M S, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174-188 [18] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. International Journal of Computer Vision, 1988, 1(4): 321-331 [19] Kehl R, Bray M, Van Gool L. Full body tracking from multiple views using stochastic sampling. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA: IEEE, 2005. 129-136 [20] Johansson G. Visual motion perception. Scientific American, 1975, 232(6): 76-88 [21] Gritai A, Sheikh Y, Shah M. On the use of anthropometry in the invariant analysis of human actions. In: Proceedings of the 17th International Conference on Pattern Recognition. Washington, DC: IEEE Computer Society, 2004. 923-926 [22] Gu Jun-Xia, Ding Xiao-Qing, Wang Sheng-Jin. Human 3D model-based 2D action recognition. Acta Automatica Sinica, 2010, 36(1): 46-53(谷军霞, 丁晓青, 王生进. 基于人体行为3D模型的2D行为识别. 自动化学报, 2010, 36(1): 46-53) [23] Bhuyan M K. FSM-based recognition of dynamic hand gestures via gesture summarization using key video object planes. International Journal of Computer and Communication Engineering, 2012, 1(6): 248-259 [24] Lv F, Nevatia R. Single view human action recognition using key pose matching and viterbi path searching. In: Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN: IEEE, 2007. 1-8 [25] Tuytelaars T, Mikolajczyk K. Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, 2008, 3(3): 177-280 [26] Rosten E, Drummond T. Fusing points and lines for high performance tracking. In: Proceedings of the IEEE International Conference on Computer Vision. Beijing: IEEE, 2005. 1508-1515 [27] Zitov [28] B, Flusser J. Image registration methods: a survey. Image and Vision Computing, 2003, 21(11): 977-1000 [29] Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110 [30] Rosten E, Porter R, Drummond T. Faster and better: a machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(1): 105-119 [31] Dunham J G. Optimum uniform piecewise linear approximation of planar curves. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(1): 67-75
点击查看大图
计量
- 文章访问数: 1923
- HTML全文浏览量: 64
- PDF下载量: 1100
- 被引次数: 0