A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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Computer architecture is evolving rapidly, and the trend is moving from a single, fast execution unit with large memory space to several execution units with small local memory. Nowadays, even consumer-level systems commonly possess multi-core processors. These lower power-consuming, high-performing multi-core systems are progressively replacing high energy-consumption desktop computers with great success, and the industry predicts that future computing systems will benefit even more from this scalable technology. This evolution is highly beneficial for data-parallel programs, where independent data processing takes place. An example of a future platform in current use is the massively parallel modern graphics processing unit (GPU), with lots of execution units, small and fast memory for each processing core, and high memory bandwidth. Using this platform for demonstration purposes, we can test algorithmic approaches that would scale well to new generations of desktop computers. It is interesting to devise new algorithmic approaches now in fields that require computational demanding tasks, in order to adapt them to new, and future, platforms. In this proposal, we use this underlying idea to propose a parallel, scalable face detection system based on a novel combination of template tracking, skin color detection and a particle filter tracking method.