Abstract
This chapter presents two approaches for addressing the challenges of processing and analysis for Big image or video data. The first approach exploits the intrinsic data-parallel nature of common image processing techniques for processing large images or dataset of images in a distributed manner on a multi-node cluster. The implementation is done using Apache Hadoop’s MapReduce framework and Hadoop Image Processing Interface (HIPI) which facilitates efficient and high-throughput image processing. It also includes a description of a Parallel Image Processing Library (ParIPL) developed by the authors on this framework which is aimed to significantly simplify image processing using Hadoop. The library exploits parallelism at various levels—frame level and intra-frame level. The second approach uses high-end GPUs for efficient parallel implementation of specialized applications with high performance and real-time processing requirements. Parallel implementation of video object detection algorithm, which is the fundamental step in any surveillance-related analysis, is presented on GPU architecture along with fine-grain optimization techniques and algorithm innovation. Experimental results show significant speedups of the algorithms resulting in real-time processing of HD and panoramic resolution videos.
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Kumar, P., Bodade, A., Kumbhare, H., Ashtankar, R., Arsh, S., Gosar, V. (2019). Parallel and Distributed Computing for Processing Big Image and Video Data. In: Seng, K., Ang, Lm., Liew, AC., Gao, J. (eds) Multimodal Analytics for Next-Generation Big Data Technologies and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-97598-6_14
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