Implementation and optimization of human tracking system using ARM embedded platform

Computer vision is a field that includes methods for acquiring, processing, analyzing and understanding images. In the embedded world, computer vision applications have to fight with limited processing power and limited resources to achieve optimized algorithms and high performance. This paper presents work on implementing a human tracking system on both Intel based PC platform and embedded systems to optimize the algorithms for high performance. The algorithms are benchmarked on the Intel platform processor and BeagleBoard xM baed on low-power Texas Instruments (TI) DM3730 ARM processor. Functions and library in OpenCV which developed by Intel Corporation was utilized for building the human tracking algorithms.