Real-time moving object detection algorithm on high-resolution videos using GPUs

• Published in: Journal of real-time image processing 2016-01, Vol.11 (1), p.93-109
• Main Authors: Kumar, Praveen, Singhal, Ayush, Mehta, Sanyam, Mittal, Ankush
• Format: Article
• Language: English
• Subjects: Algorithms; Bandwidths; Cameras; Computer Graphics; Computer Science; Frames per second; Graphics processing units; High resolution; Image Processing and Computer Vision; Labeling; Microprocessors; Morphology; Moving object recognition; Multimedia Information Systems; Original Research Paper; Parallel processing; Pattern Recognition; Real time; Signal,Image and Speech Processing; Surveillance; Video; Workloads
• ISSN: 1861-8200; 1861-8219
• DOI: 10.1007/s11554-012-0309-y

Modern imaging sensors with higher megapixel resolution and frame rates are being increasingly used for wide-area video surveillance (VS). This has produced an accelerated demand for high-performance implementation of VS algorithms for real-time processing of high-resolution videos. The emergence of multi-core architectures and graphics processing units (GPUs) provides energy and cost-efficient platform to meet the real-time processing needs by extracting data level parallelism in such algorithms. However, the potential benefits of these architectures can only be realized by developing fine-grained parallelization strategies and algorithm innovation. This paper describes parallel implementation of video object detection algorithms like Gaussians mixture model (GMM) for background modelling, morphological operations for post-processing and connected component labelling (CCL) for blob labelling. Novel parallelization strategies and fine-grained optimization techniques are described for fully exploiting the computational capacity of CUDA cores on GPUs. Experimental results show parallel GPU implementation achieves significant speedups of ~250× for binary morphology, ~15× for GMM and ~2× for CCL when compared to sequential implementation running on Intel Xeon processor, resulting in processing of 22.3 frames per second for HD videos.