Abstract
This paper demonstrates how latency tolerant parallel particle swarm optimization can be used to achieve real-time full-body motion tracking. The tracking is realized using multi-view images and articulated 3D model with a truncated cones-based representation of the body. Each CPU core computes fitness score for a single camera. On each node the algorithm uses the current temporary best fitness value without waiting for the global best one from cooperating sub-swarms. The algorithm runs at 10 Hz on eight PC nodes connected by 1 GigE.
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Kwolek, B., Krzeszowski, T., Wojciechowski, K. (2011). Real-Time Multi-view Human Motion Tracking Using 3D Model and Latency Tolerant Parallel Particle Swarm Optimization. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2011. Lecture Notes in Computer Science, vol 6930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24136-9_15
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DOI: https://doi.org/10.1007/978-3-642-24136-9_15
Publisher Name: Springer, Berlin, Heidelberg
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