Distress Signal Recognition Using Pose Estimation and Neural Networks

Publication Date

1-1-2023

Document Type

Conference Proceeding

Publication Title

Proceedings - 2023 11th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2023

DOI

10.1109/MobileCloud58788.2023.00010

First Page

25

Last Page

32

Abstract

Detecting real-Time distress signals in public places can help authorities react promptly in life-Threatening circumstances or violent encounters and assist those in distress. The most common way to ask for help is to shout or flap your hands above your head, often called a 'waving' action. This study aims to deploy distress signal recognition software on existing surveillance systems or cameras to automatically identify risky situations and alert the authorities. This will reduce human interaction and the constant monitoring of numerous surveillance feeds. In this paper, we propose two different methods for distress signal recognition. Both approaches use pose estimation models, which are used to identify and locate skeletal points on the human body. In the first approach, we propose an algorithm that detects a series of features that are used to identify a distress signal action. In the second approach, we propose a neural network-based approach to recognize distress signals. The neural network is trained on a dataset that we have constructed by identifying major features calculated using the skeletal points from the pose estimation model. Our proposed approach uses significantly fewer training data compared to the traditional convolutional neural network (CNN) approaches that use image or video data for action recognition. In this paper, we compare and assess the two approaches and demonstrate their effectiveness on a real-world dataset.

Keywords

action detection, deep learning, distress signal recognition, neural network, pose estimation

Department

Computer Engineering

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