Titlebook: Video Surveillance for Sensor Platforms; Algorithms and Archi Mayssaa Al Najjar,Milad Ghantous,Magdy Bayoumi Book 2014 Springer Science+Bus

來就得意

Object Tracking, games, and surveillance systems. Tracking and detection are very critical in monitoring systems as their accuracy greatly impacts the eventual success or failure of later scene analysis. This chapter reviews the main approaches previously proposed, top-down known as filtering and data association a

Mirage

Hysteresis Thresholding, chapter, we present a unified compact architecture that couples Hysteresis Thresholding with connected component analysis and Object Feature Extraction (HT-OFE) in a single pass over the image. Two versions are developed: a high-accuracy pixel-based architecture and a faster block-based one at the

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Hardware Architecture Assist for Critical Components,hese critical sections are viewed as bottleneck especially when real-time response is desired. Specialized hardware solutions are developed to accelerate critical low-level such operations. In this chapter, two hardware architectures are presented to complement image processing algorithms discussed

Innovative

Conclusion, multiple collaborative visual nodes with sensing, data processing, and communication capabilities. This enables them to collect large volumes of images about surveyed scenes, process them, and transmit extracted data to the base station for further analysis. VSNs are faced with several challenges g

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Book 2014in security, monitoring and integration in 3rd Generation Surveillance Systems. All algorithms are evaluated through experimental and simulation results and a parallel and pipelined efficient architecture for implementing the algorithms is described.

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locus-ceruleus

Image Registration, register two images in a multi-resolution pyramidal scheme, AMIR offers an automatic multimodal image registration based on gradient descent optimization. Both algorithms exhibit comparable performance to state of the art approaches while decreasing the processing burden.

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Object Detection, Mixture of Gaussian modeling, named HS-MoG. The scheme provides accurate results for outdoor scenes with clutter motion in the background while requiring fewer operations than the original Mixture of Gaussian scheme.

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Hysteresis Thresholding,hen made to keep or discard these pixels. Processing on the fly enables faster results and avoids additional passes for handling weak pixels and extracting object features. Moreover, labels are reused so only one compact row is buffered and memory requirements are drastically reduced.

卜聞

Hardware Architecture Assist for Critical Components,ficient hardware implementation for image decomposition based on Discrete Wavelet Transform. Both architectures exhibit higher performance than their software counterpart and hence help in alleviating the burden off the processing tasks.