Action Recognition Using Single-Pixel Time-of-Flight Detection.

Ikechukwu Ofodile,Sandhra-Mirella Valdma,Sergey Omelkov,Gholamreza Anbarjafari,Egils Avots,Kerttu Maria Peensoo,Sergio Escalera,Heli Valtna-Lukner,Andreas Valdmann,Albert Clapés,Cagri Ozcinar,Ahmed Helmi

doi:10.3390/e21040414

Abstract

Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene. Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent neural network.

Highlights

Action is a spatiotemporal sequence of patterns [1,2,3,4,5,6]
In this work, which is a feasibility study of a novel setup and methodology for conducting action recognition, we propose and demonstrate an action recognition scheme based on a single-pixel direct time-of-flight detection
We develop a new methodology for action recognition without using any data which can rise a privacy issue

Summary

Introduction

The ability to detect movement and recognise human actions and gestures would enable advanced human to machine interaction in wide scope of novel applications in the field of robotics from autonomous vehicles, surveillance for security or care-taking to entertainment. In the field of machine vision, the majority of effort has been put into recognising human action from video sequences [7,8,9], because overwhelmingly imaging devices mimic the human-like perception of the surroundings and video format is most widely available. Videos are a sequence of two-dimensional intensity patters, captured by using an imaging lens projecting the scene to a two-dimensional detector array (a charge coupled device (CCD) device, for example). Entropy 2019, 21, 414 creatures, the ever growing field of robotics has run into major difficulties while trying to recognise objects, their actions, and their distances from two-dimensional images. Processing the data is computationally demanding, and depth information is not unanimously retrievable

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Conclusion