M Tech Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3

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Subject: search.filters.subject.Computer Graphics

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    Hardware-software design of real-time MPEG-2 video encoder
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Sodani, Arpit; Dubey, Rahul
    The goal of this thesis is to analyze how MPEG-2 encoder can be optimized for real-time streaming applications Hw/Sw design re-configurable platform is chosen, where part of algorithm runs on CPU or on re-programmable hardware as a hardware accelerator. The structures is based on block level pipelining(BLP) where each frame is divided in 8X8 pixel blocks and each block is processed through the MPEG-2 video compression algorithm designed in a pipeline fashion and optimized so at to achieve maximum throughput. Here, we have designed an encoder for a Xilinx Zynq7000 series SoPC FPGA platform named as Zedboard. Initially, the encoder is designed in C and run on ARM cortex A-9 processor. This code is then profiled for ARM processor based on computational requirements. The results are analyzed and the computationally intensive subsystems are implemented as hardware accelerators to attain the desired features.
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    Low-rank and sparse decomposition of compressively-sensed matrices: applications to surveillance video processing
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Lekshmi, Ramesh; Shah, Pratik; Sinha, V. P.
    Detection, recognition and tracking are three of the primary tasks involved in surveillance video processing. Given the huge amount of data generated by surveillance systems, it is desirable to use compressed sensing based techniques for acquisition and subsequent processing of videos. For compressively-sensed videos, the task of object detection can be formulated as a matrix decomposition problem, namely, decompose the video volume matrix into a low-rank background matrix and a sparse foreground matrix given a small set of linear measurements corresponding to the video volume matrix. In this thesis, we first look at three existing algorithms for low-rank and sparse matrix decomposition: Alternating Direction Method of Multipliers (ADMM), Frank-Wolfe-Projection (FW-P) and Sparse and low Rank decomposition via Compressive Sensing (SpaRCS). These algorithms do not make use of any additional structure in the data. In the case of surveillance videos, we observe that the foreground is connected in addition to being sparse. Based on this observation, we propose a regularized version of the SpaRCS algorithm, Regularized-SpaRCS (R-SpaRCS), which exploits the fact that the foreground component in surveillance videos exhibits connectedness. R-SpaRCS is a model-based greedy algorithm that introduces a support regularization step into the SpaRCS algorithm. Experiments performed on surveillance video datasets show that R-SpaRCS achieves a given recovery RSNR faster than the SpaRCS algorithm.