Detection of salient moving object has great potentials in activity recognition, scene understanding, etc. However techniques to characterizing the object in fine granularity have not been well developed in real applications due to the computational intensity. The emerging multi-core technology in hardware design provides an opportunity for the compute intensive algorithms to boost speed in parallel. This paper proposed a scalable approach to detecting salient moving object which is designed inherently for parallelization. To characterize the object in fine granularity, we extract color-texture homogenous regions as the basic processing unit by image segmentation. To identify salient object, we generate probabilistic template by learning the space-time context. The parallel algorithm is implemented using OpenMP. Evaluations have been carried out on sports, news, and home video data. For the CIF size image, we get processing speed of 51.1 frames per second and near linear speed up on an eight-core machine. It indicates that the algorithm parallelization is a promising solution for practical applications in the multimedia field.
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