The DOI matches its Crossref record.
ADAS and Video Surveillance Analytics System Using Deep Learning Algorithms on FPGA
Abstract
Deep learning algorithms, such as CNN (Convolutional Neural Network), could provide high accuracy for great number of applications including ADAS (Advanced Driver Assistance System) and video surveillance analytics. Considering processing speed and energy efficiency, FPGA is a good hardware to construct customized CNN solution. In this demo session, we want to benefit from hardware technology, and show a fast speed and accurate video analytics system using state-of-the-art deep learning algorithms running on low power FPGA. This system could process 16 channels of continuous input video with the resolution of 1080p. Two functionalities could be easily switched by just clicking a button in this live demo: one ADAS system for vehicle, non-motorized vehicle, and pedestrian detection, tracking, and attributes analytics; and the other video surveillance system for face detection and recognition. The deep learning algorithms used are SSD and densebox for two kinds of objects' detection, which have state-of-the-art accuracy. The FPGA used is Xilinx MPSoC ZU9, and the whole board including this FPGA only cost about 50 Watts with Peak performance at 5.6 TOPS.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The ADAS and video surveillance analytics systems using deep learning algorithms on FPGA demonstration system has three main features that show the fast speed, high flexibility and low power consumption of the deep learning processing with FPGA.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [73]
In the citing paper
“SSD and Densebox are used for the deep learning algorithm with the highest accuracy.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [73]
In the citing paper
“The ADAS system can be used on vehicles, for the detection of pedestrians, for tracking purposes or for attributes analytics, while for video surveillance it is implemented in face detection and recognition.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [73]
In the citing paper
“Level-3 ADAS functionalities can be made possible where the cameras have a surrounding view of the vehicle.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [73]
In the citing paper
“The deploying of the CNN network architecture can be made in several seconds for the FPGA system, as the customized design of the deep learning processing unit that sped up the CNN models.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [73]
In the citing paper
“Another application with a convolutional neural network is implemented together with FPGA in the advanced driver assistance system (ADAS) and video surveillance analytics application, for their energy efficiency and computational processing speed [73].”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [73]
In the citing paper
“Sixteen channels can be processed by the system with continuous input of video of 1080 p resolution.”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [73]
Citations
Cited by
Sorted by citations
Embedded Intelligence on FPGA: Survey, Applications and Challenges
Seng, K. P., Lee, P. J., & Ang, L. M. (2021) · Electronics · doi:10.3390/electronics10080895 · Cited by 105
“Next, the facial recognition in the conference hall is performed by implementing the cameras to capture the real-time video, and then FPGA is used to analyze and display the result on the screen [73].”
Method
Metadata
- Authors
- Shan, Y.
- Corresponding author
- Shan, Yi
- Venue
- 2018 28th International Conference on Field Programmable Logic and Applications (FPL)
- Publisher
- IEEE
- Published
- 2018-08
- OpenAlex
- W2904430334
- Cited by
- 10
- References
- 0
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 2 · 2020: 5 · 2019: 1
- FWCI
- 0.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Field Programmable Logic and Applications (FPL) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Hardware and Architecture › Embedded Systems Design Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Embedded Systems and FPGA Applications
- Keywords
- driver assistance systems, FPGA acceleration, convolutional neural networks, object detection, SSD, pedestrian detection, vehicle detection, face detection, face recognition, low-power video processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
