A Low-power Computer Vision Engine for Video Surveillance

New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people’s movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system.

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Low-Power Computer Vision

10.1201/9781003162810 ◽

2022 ◽

Author(s):

George K. Thiruvathukal ◽

Yung-Hsiang Lu ◽

Jaeyoun Kim ◽

Yiran Chen ◽

Bo Chen

Keyword(s):

Computer Vision ◽

Low Power

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Multimodal Abandoned/Removed Object Detection for Low Power Video Surveillance Systems

2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance ◽

10.1109/avss.2009.72 ◽

2009 ◽

Cited By ~ 30

Author(s):

Michele Magno ◽

Federico Tombari ◽

Davide Brunelli ◽

Luigi Di Stefano ◽

Luca Benini

Keyword(s):

Low Power ◽

Object Detection ◽

Video Surveillance ◽

Surveillance Systems

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A Survey on FPGA-Based Sensor Systems: Towards Intelligent and Reconfigurable Low-Power Sensors for Computer Vision, Control and Signal Processing

Sensors ◽

10.3390/s140406247 ◽

2014 ◽

Vol 14 (4) ◽

pp. 6247-6278 ◽

Cited By ~ 45

Author(s):

Gabriel García ◽

Carlos Jara ◽

Jorge Pomares ◽

Aiman Alabdo ◽

Lucas Poggi ◽

...

Keyword(s):

Computer Vision ◽

Signal Processing ◽

Low Power ◽

Sensor Systems ◽

Vision Control

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Security Applications Using Computer Vision

Image Processing ◽

10.4018/978-1-4666-3994-2.ch055 ◽

2013 ◽

pp. 1093-1110

Author(s):

Sreela Sasi

Keyword(s):

Computer Vision ◽

Power Plant ◽

Video Surveillance ◽

Homeland Security ◽

Port Security ◽

Video Cameras ◽

Security Applications ◽

Wide Range ◽

Cargo Inspection ◽

Computer Vision Systems

Computer vision plays a significant role in a wide range of homeland security applications. The homeland security applications include: port security (cargo inspection), facility security (embassy, power plant, bank), and surveillance (military or civilian), et cetera. Video surveillance cameras are placed in offices, hospitals, banks, ports, parking lots, parks, stadiums, malls, train stations, airports, et cetera. The challenge is not for acquiring surveillance data from these video cameras, but for identifying what is valuable, what can be ignored, and what demands immediate attention. Computer vision systems attempt to construct meaningful and explicit descriptions of the environment or scene captured in an image. A few Computer Vision based security applications are presented here for securing building facility, railroad (Objects on railroad, and red signal detection), and roads.

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