Abstract
Real-time applications of image and video processing algorithms have seen explosive growth in number and complexity over the past decade driven by consumer, scientific and defense applications exploiting inexpensive digital video cameras and networked computing device. This growth has opened up different alternatives to greatly enhance the surveillance capabilities using new architectures and parallelization strategies developed due to the increased accessibility of multicore, multi-threaded processors along with general purpose graphics processing units (GPUs). In this paper, we present a new implementation of a moving human detection algorithm on GPU based on the programming language CUDA. In our approach, the moving object is extracted by background subtraction based on the GMM (Gaussian Mixture Model) on GPU. Then, two complementary features are extracted for moving object classification. They are contour-based description: FD or Fourier Descriptor and region-based description: HOG or Histogram of Oriented Gradient. Both descriptors will then be effectively integrated to SVM (Support Vector Machine), which is able to provide the posterior probability, to achieve better performance. The implementation of such algorithm on a GPU allows a great performance in terms of execution time since it is 19 times faster than that on a CPU. Experimental results show also that the proposed approach outperforms some existing techniques and can detect pedestrians in real-time effectively.