Abstract
In this study we describe a method for 3D trajectory based recognition of and discrimination between different working actions in an industrial environment. A motion-attributed 3D point cloud represents the scene based on images of a small-baseline trinocular camera system. A two-stage mean-shift algorithm is used for detection and 3D tracking of all moving objects in the scene. A sequence of working actions is recognised with a particle filter based matching of a non-stationary Hidden Markov Model, relying on spatial context and a classification of the observed 3D trajectories. The system is able to extract an object performing a known action out of a multitude of tracked objects. The 3D tracking stage is evaluated with respect to its metric accuracy based on nine real-world test image sequences for which ground truth data were determined. An experimental evaluation of the action recognition stage is conducted using 20 real-world test sequences acquired from different viewpoints in an industrial working environment. We show that our system is able to perform 3D tracking of human body parts and a subsequent recognition of working actions under difficult, realistic conditions. It detects interruptions of the sequence of working actions by entering a safety mode and returns to the regular mode as soon as the working actions continue.
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References
Black, M.J., Jepson, A.D.: A probabilistic framework for matching temporal trajectories: Condensation-based recognition of gestures and expressions. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 909–924. Springer, Heidelberg (1998)
Bradski, G.R.: Real time face and object tracking as a component of a perceptual user interface. In: Workshop on Appl. of Computer Vision, pp. 214–219 (1998)
Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time Tracking of Non-Rigid Objects Using Mean Shift. In: IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 142–149 (2000)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–577 (2003)
Gehrig, S.K., Eberli, F., Meyer, T.: A Real-Time Low-Power Stereo Vision Engine Using Semi-Global Matching. In: Fritz, M., Schiele, B., Piater, J.H. (eds.) ICVS 2009. LNCS, vol. 5815, pp. 134–143. Springer, Heidelberg (2009)
Hahn, M., Krüger, L., Wöhler, C.: 3D action recognition and long-term prediction of human motion. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 23–32. Springer, Heidelberg (2008)
Hahn, M., Krüger, L., Wöhler, C., Kummert, F.: 3D action recognition in an industrial environment. In: Ritter, H., Sagerer, G., Dillmann, R., Buss, M. (eds.) Proc. 3rd Int. Workshop on Human-Centered Robotic Systems, Cognitive Systems Monographs, vol. 6, pp. 141–150. Springer, Heidelberg (2009)
Hahn, M., Krüger, L., Wöhler, C., Sagerer, G., Kummert, F.: Spatio-temporal 3D Pose Estimation and Tracking of Human Body Parts in an Industrial Environment. In: Oldenburger 3D-Tage (2010)
Krüger, L., Wöhler, C.: Accurate chequerboard corner localisation for camera calibration and scene reconstruction. Submitted to Pattern Recog. Lett. (2009)
Li, Z., Fritsch, J., Wachsmuth, S., Sagerer, G.: An object-oriented approach using a top-down and bottom-up process for manipulative action recognition. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 212–221. Springer, Heidelberg (2006)
Tyagi, A., Keck, M., Davis, J.W., Potamianos, G.: Kernel-Based 3D Tracking. In: IEEE Conf. on Computer Vision and Pattern Recognition (2007)
Wedel, A., Pock, T., Zach, C., Bischof, H., Cremers, D.: An improved algorithm for TV-L1 optical flow computation. In: Dagstuhl Visual Motion Analysis Workshop (2008)
Wedel, A., Rabe, C., Vaudrey, T., Brox, T., Franke, U., Cremers, D.: Efficient dense scene flow from sparse or dense stereo data. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 739–751. Springer, Heidelberg (2008)
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Hahn, M., Quronfuleh, F., Wöhler, C., Kummert, F. (2010). 3D Mean-Shift Tracking of Human Body Parts and Recognition of Working Actions in an Industrial Environment. In: Salah, A.A., Gevers, T., Sebe, N., Vinciarelli, A. (eds) Human Behavior Understanding. HBU 2010. Lecture Notes in Computer Science, vol 6219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14715-9_11
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DOI: https://doi.org/10.1007/978-3-642-14715-9_11
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