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JACIII Vol.23 No.4 pp. 735-742
doi: 10.20965/jaciii.2019.p0735
(2019)

Paper:

Optical Flow for Real-Time Human Detection and Action Recognition Based on CNN Classifiers

Satoshi Hoshino and Kyohei Niimura

Department of Mechanical and Intelligent Engineering, Graduate School of Engineering, Utsunomiya University
7-1-2 Yoto, Utsunomiya, Tochigi 321-8585, Japan

Received:
October 5, 2018
Accepted:
February 19, 2019
Published:
July 20, 2019
Keywords:
robot vision, generic object recognition, real-time image processing, CNN, optical flow
Abstract
Optical Flow for Real-Time Human Detection and Action Recognition Based on CNN Classifiers

Real-time human detection and action recognition for multiple persons

Mobile robots equipped with camera sensors are required to perceive surrounding humans and their actions for safe and autonomous navigation. In this work, moving humans are the target objects. For robot vision, real-time performance is an important requirement. Therefore, we propose a robot vision system in which the original images captured by a camera sensor are described by optical flow. These images are then used as inputs to a classifier. For classifying images into human and not-human classifications, and the actions, we use a convolutional neural network (CNN), rather than coding invariant features. Moreover, we present a local search window as a novel detector for clipping partial images around target objects in an original image. Through the experiments, we ultimately show that the robot vision system is able to detect moving humans and recognize action in real time.

Cite this article as:
S. Hoshino and K. Niimura, “Optical Flow for Real-Time Human Detection and Action Recognition Based on CNN Classifiers,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.4, pp. 735-742, 2019.
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Last updated on Nov. 18, 2019