Real-time, frame-rate face detection on a configurable hardware system (poster abstract)

An improved SPH method for water fluid simulation was proposed in this paper. We used higher order splines to improve the stability, and two layers virtual particles to solve particle inconsistency problem near the boundary area. Due to the improvements paid more attention on details, we obtained more realistic and stalbe effect, which was different with previous works. Experimental results showed that our method was realistic, and could achieve real-time frame rate when particle amount was less than 10000.

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Automatic Hyoid Bone Tracking in Real-Time Ultrasound Swallowing Videos Using Deep Learning Based and Correlation Filter Based Trackers

Sensors ◽

10.3390/s21113712 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3712

Author(s):

Shurui Feng ◽

Queenie-Tsung-Kwan Shea ◽

Kwok-Yan Ng ◽

Cheuk-Ning Tang ◽

Elaine Kwong ◽

...

Keyword(s):

Deep Learning ◽

Real Time ◽

Hyoid Bone ◽

Learning Algorithm ◽

Time Frame ◽

Frame Rate ◽

Average Error ◽

Ultrasound Images ◽

Deep Learning Algorithm ◽

Reliable Performance

(1) Background: Ultrasound provides a radiation-free and portable method for assessing swallowing. Hyoid bone locations and displacements are often used as important indicators for the evaluation of swallowing disorders. However, this requires clinicians to spend a great deal of time reviewing the ultrasound images. (2) Methods: In this study, we applied tracking algorithms based on deep learning and correlation filters to detect hyoid locations in ultrasound videos collected during swallowing. Fifty videos were collected from 10 young, healthy subjects for training, evaluation, and testing of the trackers. (3) Results: The best performing deep learning algorithm, Fully-Convolutional Siamese Networks (SiamFC), proved to have reliable performance in getting accurate hyoid bone locations from each frame of the swallowing ultrasound videos. While having a real-time frame rate (175 fps) when running on an RTX 2060, SiamFC also achieved a precision of 98.9% at the threshold of 10 pixels (3.25 mm) and 80.5% at the threshold of 5 pixels (1.63 mm). The tracker’s root-mean-square error and average error were 3.9 pixels (1.27 mm) and 3.3 pixels (1.07 mm), respectively. (4) Conclusions: Our results pave the way for real-time automatic tracking of the hyoid bone in ultrasound videos for swallowing assessment.

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A web-based application for face detection in real-time images and videos

Journal of Physics Conference Series ◽

10.1088/1742-6596/2161/1/012071 ◽

2022 ◽

Vol 2161 (1) ◽

pp. 012071

Author(s):

Mehul Arora ◽

Sarthak Naithani ◽

Anu Shaju Areeckal

Keyword(s):

Neural Network ◽

Real Time ◽

Face Detection ◽

User Interfaces ◽

Detection Algorithm ◽

Frame Rate ◽

Web Based ◽

Detection Algorithms ◽

Haar Cascade ◽

The Web

Abstract Face detection is widely used in the consumer industry such as advertising, user interfaces, video streaming apps and in many security applications. Every application has its own demands and constraints, and hence cannot be fulfilled by a single face detection algorithm. In this work, we developed an interactive web-based application for face detection in real-time images and videos. Pretrained face detection algorithms, namely Haar cascade classifier, HOG-based frontal face detector, Multi-task Cascaded Convolutional Neural Network (MTCNN) and Deep Neural Network (DNN), were used in the web-based application. A performance analysis of these face detection algorithms is done for various parameters such as different lighting conditions, face occlusion and frame rate. The web app interface can be used for an easy comparison of different face detection algorithms. This will help the user to decide on the algorithm that suits their purpose and requirements for various applications.

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Real-time FPGA-based implementation of the AKAZE algorithm with nonlinear scale space generation using image partitioning

Journal of Real-Time Image Processing ◽

10.1007/s11554-021-01089-9 ◽

2021 ◽

Author(s):

Parastoo Soleimani ◽

David W. Capson ◽

Kin Fun Li

Keyword(s):

Real Time ◽

Memory Management ◽

Scale Space ◽

Image Resolution ◽

Hardware Architecture ◽

Frame Rate ◽

Time Sharing ◽

Scale Invariant ◽

Nonlinear Scale Space ◽

Nonlinear Scale

AbstractThe first step in a scale invariant image matching system is scale space generation. Nonlinear scale space generation algorithms such as AKAZE, reduce noise and distortion in different scales while retaining the borders and key-points of the image. An FPGA-based hardware architecture for AKAZE nonlinear scale space generation is proposed to speed up this algorithm for real-time applications. The three contributions of this work are (1) mapping the two passes of the AKAZE algorithm onto a hardware architecture that realizes parallel processing of multiple sections, (2) multi-scale line buffers which can be used for different scales, and (3) a time-sharing mechanism in the memory management unit to process multiple sections of the image in parallel. We propose a time-sharing mechanism for memory management to prevent artifacts as a result of separating the process of image partitioning. We also use approximations in the algorithm to make hardware implementation more efficient while maintaining the repeatability of the detection. A frame rate of 304 frames per second for a $$1280 \times 768$$ 1280 × 768 image resolution is achieved which is favorably faster in comparison with other work.

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