Research on elevator passenger fall detection based on machine vision

Liu, Shaofeng; An, Ziliang; Wang, Ning; Bai, Dingsong; Yu, Xintong

doi:10.1088/1755-1315/791/1/012108

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sun et al [7] introduced a detection approach based on the kinetic energy of corners to identify instances of aggression among elevator occupants. In a similar vein, Liu et al [8] harnessed multi-feature fusion, combined with machine vision, to detect falls of passengers within the elevator confines. While these strategies have shown a capability to identify unusual human behaviors, they are beset with challenges such as inadequate feature extraction capabilities from images and videos and the failure to effectively model temporal information.…”

Section: Introductionmentioning

confidence: 99%

A Model for Detecting Abnormal Elevator Passenger Behavior Based on Video Classification

Lei,

Sun,

Fang

et al. 2024

Electronics

View full text Add to dashboard Cite

In the task of human behavior detection, video classification based on deep learning has become a prevalent technique. The existing models are limited due to an inadequate understanding of behavior characteristics, which restricts their ability to achieve more accurate recognition results. To address this issue, this paper proposes a new model, which is an improvement upon the existing PPTSM model. Specifically, our model employs a multi-scale dilated attention mechanism, which enables the model to integrate multi-scale semantic information and capture characteristic information of abnormal human behavior more effectively. Additionally, to enhance the characteristic information of human behavior, we propose a gradient flow feature information fusion module that integrates high-level semantic features with low-level detail features, enabling the network to extract more comprehensive features. Experiments conducted on an elevator passenger dataset containing four abnormal behaviors (door picking, jumping, kicking, and door blocking) show that the top-1 Acc of our model is improved by 10% compared to the PPTSM model, reaching 95%. Moreover, experiments with four publicly available datasets(UCF24, UCF101, HMDB51, and the Something-Something-v1 dataset) demonstrate that our method achieves results superior to PPTSM by 6.8%, 6.1%, 21.2%, and 3.96%, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

A Model for Detecting Abnormal Elevator Passenger Behavior Based on Video Classification

Lei,

Sun,

Fang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Currently, significant research has been conducted worldwide on recognizing passenger behavior inside elevator cabins. Liu et al [5] introduced a falling detection algorithm based on machine vision and multi-feature fusion. However, the recognition performance of their method needs improvement, particularly in cases of occlusion.…”

Section: Introductionmentioning

confidence: 99%

Falling Behavior Detection System for Elevator Passengers Based on Deep Learning and Edge Computing

Chen,

Zhao,

Fan

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Due to factors such as dense populations and narrow viewing angles, previous deep-learning models for detecting passenger behavior in elevators often lack effectiveness. Traditional cloud-based data transmission methods have issues with high latency, high resource usage, and privacy threats, particularly during periods of high usage. To address these issues, we proposed a falling behavior detection system for elevator passengers based on deep learning and edge computing. A two-stream neural network model improved by 3D ResNet is presented, which utilizes edge computing for elevator passenger fall detection. Our homemade dataset of elevator passenger falling behavior is utilized to train and evaluate the system. The results demonstrate that the system is effective in detecting passengers’ falling behavior in elevators, with an average accuracy of 89.2%. The feasibility of the system in an elevator is also verified, and it has performed well. The application of this system in this field holds significant research value.

show abstract

“…Existing fall detection methods are divided into those based on environmental equipment [7], those based on wearable sensors [8], and those based on computer vision technology [9]. Assistant-based falls detection usually relies on some kind of wearable device, which has a variety of sensors.…”

Section: Introductionmentioning

confidence: 99%

Fall Detection Method for Embedded Devices

Liang¹,

Liu²,

Tong³

et al. 2022

jist

View full text Add to dashboard Cite

The challenges of the aging population is becoming more and more prominent worldwide. Among them, in the face of the elderly fall phenomenon, human fall detection technology research and development has practical application value. Because of a large number of network parameters in the field of fall detection and the limited computing power of embedded devices, which makes it difficult to run on the embedded platform, this paper proposes an OT-YOLOV3 (OpenCV+Tiny-YOLOV3) fall detection method. In this method, Gaussian processing and other operations are used to preprocess the fallen image to avoid the influence of the angle change of the image on the recognition result. Then, the feature extraction network in Tiny-YOLOV3 was replaced by the MobileNet network to increase the number of network layers and reduce the number of parameters and calculations in the model. At the same time, the multi-scale prediction method was used to improve detection accuracy. Experimental results show that the accuracy of the proposed model is 10% higher than that of the YOLOV3 (You Only Look Once Version three) model, 4% higher than that of the Tiny-YOLOV3 model, 3% higher than that of the YOLOV3 model, 3% higher than that of Tiny-YOLOV3 model, and the model size is only 45% of that of YOLOV3 model and 65% of Tiny-YOLOV3. Compared with YOLOV3 and Tiny-YOLOV3 processing methods, the drop recognition effect is significantly improved and the model memory is reduced, which meets the requirements of real-time and efficient detection for embedded devices.

show abstract

Research on elevator passenger fall detection based on machine vision

Cited by 3 publications

References 0 publications

A Model for Detecting Abnormal Elevator Passenger Behavior Based on Video Classification

A Model for Detecting Abnormal Elevator Passenger Behavior Based on Video Classification

Falling Behavior Detection System for Elevator Passengers Based on Deep Learning and Edge Computing

Fall Detection Method for Embedded Devices

Contact Info

Product

Resources

About