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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6219 LNCS, pp. 101–112). https://doi.org/10.1007/978-3-642-14715-9_11
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