Proceedings ArticleDOI
Towards a robust, real-time face processing system using CUDA-enabled GPUs
Bharatkumar Sharma,Rahul Thota,Naga Vydyanathan,Amit Kale +3 more
- pp 368-377
TLDR
This paper designs and develops optimized parallel implementations of face detection and tracking algorithms on graphics processors using the Compute Unified Device Architecture (CUDA), a C-based programming model from NVIDIA.Abstract:
Processing of human faces finds application in various domains like law enforcement and surveillance, entertainment (interactive video games), information security, smart cards etc. Several of these applications are interactive and require reliable and fast face processing. A generic face processing system may comprise of face detection, recognition, tracking and rendering. In this paper, we develop a GPU accelerated real-time and robust face processing system that does face detection and tracking. Face detection is done by adapting the Viola and Jones algorithm that is based on the Adaboost learning system. For robust tracking of faces across real-life illumination conditions, we leverage the algorithm proposed by Thota and others, that combines the strengths of Adaboost and an image based parametric illumination model. We design and develop optimized parallel implementations of these algorithms on graphics processors using the Compute Unified Device Architecture (CUDA), a C-based programming model from NVIDIA. We evaluate our face processing system using both static image databases as well as using live frames captured from a firewire camera under realistic conditions. Our experimental results indicate that our parallel face detector and tracker achieve much greater detection speeds as compared to existing work, while maintaining accuracy. We also demonstrate that our tracking system is robust to extreme illumination conditions.read more
Citations
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Department of Computer Science and Engineering
TL;DR: In this article, the authors present a survey of postgraduate students: Vladimír Arnot, Daniel Čapek, Rudolf Čejka, Dao Minh, Tomá Dulík, Martin Hrubý, Radek Kočí, Petr Kotásek, Marek Křejpský and Bohuslav KŘena, Vladislav Kubíček.
Journal ArticleDOI
A systematic literature review on hardware implementation of artificial intelligence algorithms
TL;DR: This work presents a systematic literature review that focuses on exploring the available hardware accelerators for the AI and ML tools, using FPGAs, GPUs and ASICs to accelerate computationally intensive tasks.
Proceedings ArticleDOI
Boosted human head pose estimation using kinect camera
Anwar Saeed,Ayoub Al-Hamadi +1 more
TL;DR: This work presents a boosted method to estimate the head pose using Kinect camera with the help of RGB and depth images, and demonstrates that this approach compares favorably to state-of-the-art approaches.
Proceedings ArticleDOI
Real-time GPU-based face detection in HD video sequences
TL;DR: This paper presents a highly optimized Haar-based face detector that works in real time over high definition videos and achieves a sustained throughput of 35 fps under 1080p resolutions using a sliding window with step of one pixel.
Proceedings ArticleDOI
Accelerating Viola-Jones Facce Detection Algorithm on GPUs
TL;DR: An OpenCL-implementation of Viola-Jones face detection algorithm with high performance on both NVIDIA and AMD GPUs through five main techniques: warp size work granularity, persistent threads, Uberkernel, local and global queues is presented.
References
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