High resolution cameras and multi camera systems are being used in areas of video surveillance like security of public places, traffic monitoring, and military and satellite imaging. This leads to a demand for computa...High resolution cameras and multi camera systems are being used in areas of video surveillance like security of public places, traffic monitoring, and military and satellite imaging. This leads to a demand for computational algorithms for real time processing of high resolution videos. Motion detection and background separation play a vital role in capturing the object of interest in surveillance videos, but as we move towards high resolution cameras, the time-complexity of the algorithm increases and thus fails to be a part of real time systems. Parallel architecture provides a surpass platform to work efficiently with complex algorithmic solutions. In this work, a method was proposed for identifying the moving objects perfectly in the videos using adaptive background making, motion detection and object estimation. The pre-processing part includes an adaptive block background making model and a dynamically adaptive thresholding technique to estimate the moving objects. The post processing includes a competent parallel connected component labelling algorithm to estimate perfectly the objects of interest. New parallel processing strategies are developed on each stage of the algorithm to reduce the time-complexity of the system. This algorithm has achieved a average speedup of 12.26 times for lower resolution video frames(320×240, 720×480, 1024×768) and 7.30 times for higher resolution video frames(1360×768, 1920×1080, 2560×1440) on GPU, which is superior to CPU processing. Also, this algorithm was tested by changing the number of threads in a thread block and the minimum execution time has been achieved for 16×16 thread block. And this algorithm was tested on a night sequence where the amount of light in the scene is very less and still the algorithm has given a significant speedup and accuracy in determining the object.展开更多
The design, analysis and parallel implementation of particle filter(PF) were investigated. Firstly, to tackle the particle degeneracy problem in the PF, an iterated importance density function(IIDF) was proposed, wher...The design, analysis and parallel implementation of particle filter(PF) were investigated. Firstly, to tackle the particle degeneracy problem in the PF, an iterated importance density function(IIDF) was proposed, where a new term associating with the current measurement information(CMI) was introduced into the expression of the sampled particles. Through the repeated use of the least squares estimate, the CMI can be integrated into the sampling stage in an iterative manner, conducing to the greatly improved sampling quality. By running the IIDF, an iterated PF(IPF) can be obtained. Subsequently, a parallel resampling(PR) was proposed for the purpose of parallel implementation of IPF, whose main idea was the same as systematic resampling(SR) but performed differently. The PR directly used the integral part of the product of the particle weight and particle number as the number of times that a particle was replicated, and it simultaneously eliminated the particles with the smallest weights, which are the two key differences from the SR. The detailed implementation procedures on the graphics processing unit of IPF based on the PR were presented at last. The performance of the IPF, PR and their parallel implementations are illustrated via one-dimensional numerical simulation and practical application of passive radar target tracking.展开更多
文摘High resolution cameras and multi camera systems are being used in areas of video surveillance like security of public places, traffic monitoring, and military and satellite imaging. This leads to a demand for computational algorithms for real time processing of high resolution videos. Motion detection and background separation play a vital role in capturing the object of interest in surveillance videos, but as we move towards high resolution cameras, the time-complexity of the algorithm increases and thus fails to be a part of real time systems. Parallel architecture provides a surpass platform to work efficiently with complex algorithmic solutions. In this work, a method was proposed for identifying the moving objects perfectly in the videos using adaptive background making, motion detection and object estimation. The pre-processing part includes an adaptive block background making model and a dynamically adaptive thresholding technique to estimate the moving objects. The post processing includes a competent parallel connected component labelling algorithm to estimate perfectly the objects of interest. New parallel processing strategies are developed on each stage of the algorithm to reduce the time-complexity of the system. This algorithm has achieved a average speedup of 12.26 times for lower resolution video frames(320×240, 720×480, 1024×768) and 7.30 times for higher resolution video frames(1360×768, 1920×1080, 2560×1440) on GPU, which is superior to CPU processing. Also, this algorithm was tested by changing the number of threads in a thread block and the minimum execution time has been achieved for 16×16 thread block. And this algorithm was tested on a night sequence where the amount of light in the scene is very less and still the algorithm has given a significant speedup and accuracy in determining the object.
基金Project(61372136) supported by the National Natural Science Foundation of China
文摘The design, analysis and parallel implementation of particle filter(PF) were investigated. Firstly, to tackle the particle degeneracy problem in the PF, an iterated importance density function(IIDF) was proposed, where a new term associating with the current measurement information(CMI) was introduced into the expression of the sampled particles. Through the repeated use of the least squares estimate, the CMI can be integrated into the sampling stage in an iterative manner, conducing to the greatly improved sampling quality. By running the IIDF, an iterated PF(IPF) can be obtained. Subsequently, a parallel resampling(PR) was proposed for the purpose of parallel implementation of IPF, whose main idea was the same as systematic resampling(SR) but performed differently. The PR directly used the integral part of the product of the particle weight and particle number as the number of times that a particle was replicated, and it simultaneously eliminated the particles with the smallest weights, which are the two key differences from the SR. The detailed implementation procedures on the graphics processing unit of IPF based on the PR were presented at last. The performance of the IPF, PR and their parallel implementations are illustrated via one-dimensional numerical simulation and practical application of passive radar target tracking.
