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2020 | OriginalPaper | Chapter

An Efficient Solution for People Tracking and Profiling from Video Streams Using Low-Power Compute

Authors : Marius Eduard Cojocea, Traian Rebedea

Published in: Advances in Computational Collective Intelligence

Publisher: Springer International Publishing

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Abstract

Balancing between performance and speed is vital for real-time applications. Given some of the latest edge devices, such as Raspberry Pi 4, Intel Neural Compute Stick 2, or Nvidia Jetson series, edge processing can become a valid choice for deploying computer vision algorithms in real-time scenarios. Object detection and tracking are two common problems that can be solved using such algorithms, which can be deployed with reasonable performance and speed on edge devices. In this paper, we show that the YOLO architecture can be successfully used for object detection and DeepSORT for object tracking on edge devices. The objects of interest in our scenario are persons, thus indicating face detection and tracking as another problem that is solved in the scope of the paper. Using Raspberry Pi 4 and Intel Neural Compute Stick 2, object detection and tracking models can be run on edge devices, though at around half the performance and more than 10 times slower than on a GPU server.

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Metadata
Title
An Efficient Solution for People Tracking and Profiling from Video Streams Using Low-Power Compute
Authors
Marius Eduard Cojocea
Traian Rebedea
Copyright Year
2020
Book
Advances in Computational Collective Intelligence

Print ISBN: 978-3-030-63118-5

Electronic ISBN: 978-3-030-63119-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63119-2_13

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