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Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network
2020
Applied Sciences
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Inspired by the famous two-stream convolutional neural network (CNN) model, we propose a driver behavior analysis system using one spatial stream ConvNet to extract the spatial features and one temporal ...
According to the experimental results, the proposed system can increase the accuracy rate by nearly 30% compared to the two-stream CNN model with a score-level fusion. ...
In Reference [48] , Chuang et al. proposed a skeleton-based and a point-cloud approach with multiple views based on Kinect depth cameras for driver behavior recognition, and LSTM was adopted to train ...
doi:10.3390/app10061908
fatcat:6ozshja3ijbrbjxoezvelid6xq
Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
2021
Applied Sciences
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The approach implements a two-stream architecture using two separate 3D CNNs to accept a video and an optical flow stream as input to enhance the prediction performance. ...
This paper presents a CNN-based approach that efficiently detects and classifies if a video involves the abnormal human behaviors of falling, loitering, and violence within uncrowded scenes. ...
The approach stacks the horizontal d x t and vertical d y t components of the displacement vector fields to create stacks O ∈ R 224×224×2×L where O = d x t , d y t , d x t+1 , d y t+1 , . . . , d x t+L ...
doi:10.3390/app11083523
fatcat:cny7c4u4lnaynfqn3lu3te256m
Skeleton-Based Emotion Recognition Based on Two-Stream Self-Attention Enhanced Spatial-Temporal Graph Convolutional Network
2020
Sensors
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We propose a self-attention enhanced spatial temporal graph convolutional network for skeleton-based emotion recognition, in which the spatial convolutional part models the skeletal structure of the body ...
Emotion recognition has drawn consistent attention from researchers recently. ...
We proposed a novel two-stream self-attention enhanced spatial temporal graph convolutional network for emotion recognition based on the skeleton data. ...
doi:10.3390/s21010205
pmid:33396917
pmcid:PMC7795329
fatcat:pqxk3jadnrhhhkpzngfn2cuiva
Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets with Multi-stream Inputs
[article]
2018
arXiv
pre-print
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The proposed solution exploits information from multi-stream inputs, i.e., in-vehicle cameras with different fields of view and optical flows computed based on recorded images, and merges through multiple ...
Recognizing driver behaviors is becoming vital for in-vehicle systems that seek to reduce the incidence of car accidents rooted in cognitive distraction. ...
ACKNOWLEDGMENT This work was partially supported by a grant (18TLRP-B131486-02) from the Transportation and Logistics R&D Program funded by Ministry of Land, Infrastructure and Transport of the Korean ...
arXiv:1811.09128v1
fatcat:pgwnb7diybbrvczg7y3voq3v3y
Driver Behavior Recognition via Interwoven Deep Convolutional Neural Nets with Multi-stream Inputs
2020
IEEE Access
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ACKNOWLEDGMENTS This research was supported by grant no. 18TLRP-B131486-02 from the Transportation and Logistics R&D Program funded by the Ministry of Land, Infrastructure and Transport of the Korean government ...
DEEP LEARNING BASED DRIVER BEHAVIOR IDENTIFICATION Deep learning is becoming increasingly popular for identifying driver behaviors. ...
TABLE 2 : 2 Inference accuracy gained with different CNN blocks over full behaviors, before\after applying the TV scheme. ...
doi:10.1109/access.2020.3032344
fatcat:jr2e4ib3lvblhbp3o3pnd3mvue
STA-Net: A Spatial–Temporal Joint Attention Network for Driver Maneuver Recognition, Based on In-Cabin and Driving Scene Monitoring
2024
Applied Sciences
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First, we introduce a two-stream architecture for a concurrent analysis of in-cabin driver behaviors and out-cabin environmental information. ...
Existing CNN-based and transformer-based driver maneuver recognition methods face challenges in effectively capturing global and local features across temporal and spatial dimensions. ...
The contributions of this study can be summarized as follows: (1) We propose a two-stream network to extract in-cabin driver behavior and out-cabin environmental information, addressing spatiotemporal ...
doi:10.3390/app14062460
fatcat:an4lq2rruff3ffno567dwhqwma
Context-driven Multi-stream LSTM (M-LSTM) for Recognizing Fine-Grained Activity of Drivers
[chapter]
2019
Lecture Notes in Computer Science
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The proposed M-LSTM integrates these ideas under one framework, where two streams focus on appearance information with two different levels of abstractions. ...
We validate this on two challenging datasets consisting driver activities. ...
We would like to thank Taylor Smith in State Farm Corporation for providing information about their dataset. The GPU used in this research is generously donated by the NVIDIA Corporation. ...
doi:10.1007/978-3-030-12939-2_21
fatcat:k25yj72bf5hadgaumtflaptwfe
Driving Fatigue Detection Based on the Combination of Multi-Branch 3D-CNN and Attention Mechanism
2022
Applied Sciences
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In this study, a fatigue driving detection system based on a 3D convolutional neural network combined with a channel attention mechanism (Squeeze-and-Excitation module) is proposed. ...
Fatigue driving is one of the main causes of traffic accidents today. ...
Table 1 . 1 Classification accuracy of the proposed method and different classical CNN structures on four datasets (where T stands for Three-branch 3D-SE-Net and D stands for Double-branch 3D-SE-Net). ...
doi:10.3390/app12094689
fatcat:j4eweh2ucjghdnoonm4t3ieowy
A Multi-Semantic Driver Behavior Recognition Model of Autonomous Vehicles Using Confidence Fusion Mechanism
2021
Actuators
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Driver behavior cognition is significant for improving safety, comfort, and human–vehicle interaction. ...
Compared to the state-of-the-art action recognition model, our approach obtains higher accuracy, especially for behaviors with similar movements. ...
Acknowledgments: The authors appreciate the reviewers and editors for their helpful comments and suggestions in this study.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/act10090218
fatcat:hu25kqmqx5afjdsxwhhebfuuay
RGB-D Data-Based Action Recognition: A Review
2021
Sensors
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In this paper, we focus solely on data fusion and recognition techniques in the context of vision with an RGB-D perspective. ...
This review is aimed to scope current literature on data fusion and action recognition techniques and to identify gaps and future research direction. ...
Acknowledgments: The authors would like to thank the anonymous reviewers for their careful reading and valuable remarks, which have greatly helped extend the scope of this paper. ...
doi:10.3390/s21124246
pmid:34205782
pmcid:PMC8234200
fatcat:7dvocdy63rckne5yunhfsnr4p4
TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration
[article]
2022
arXiv
pre-print
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Traditional video-based human activity recognition has experienced remarkable progress linked to the rise of deep learning, but this effect was slower as it comes to the downstream task of driver behavior ...
In this work, we present a novel vision-based framework for recognizing secondary driver behaviours based on visual transformers and an additional augmented feature distribution calibration module. ...
The Video Swin Base backbone is implemented into driver activity recognition in our task by selected two clips containing 32 frames individually based on the raw video input with step size 2 and randomly ...
arXiv:2203.00927v2
fatcat:tiaymojzjnc23bven2dc2pp3za
Finger Gesture Spotting from Long Sequences Based on Multi-Stream Recurrent Neural Networks
2020
Sensors
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Gesture spotting is an essential task for recognizing finger gestures used to control in-car touchless interfaces. ...
In this paper, we address these challenges with a recurrent neural architecture for online finger gesture spotting. ...
We employ the gesture recognition method of [39] , which employs a 3D-CNN architecture based on ResNext-101 [44] . ...
doi:10.3390/s20020528
pmid:31963623
pmcid:PMC7014506
fatcat:2rquuer4xbbbvl2uaai6vqoc4u
A Lightweight Driver Drowsiness Detection System Using 3DCNN With LSTM
2023
Computer systems science and engineering
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The study was conducted on two publicly available drowsy drivers datasets named 3MDAD and YawDD. 3MDAD is mainly composed of two synchronized datasets recorded from the frontal and side views of the drivers ...
The 3DCNN-LSTM can analyze long sequences by applying the 3D-CNN to extract spatiotemporal features within adjacent frames. ...
feature. [22] CNN-LSTM Built-in 227 × 227 16 f A DDD system employs a CNN-LSTM network to extract spatial features and model temporal dynamics based on drivers' eyes features. [23] Two-stream CNNs Built-in ...
doi:10.32604/csse.2023.024643
fatcat:e2wsofpj5jhshjlfmpuga6rrs4
An Intelligent Security Classification Model of Driver's Driving Behavior Based on V2X in IoT Networks
2022
Security and Communication Networks
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Firstly, according to the driver target detection for positioning, combined with the Pose Estimation to identify the driver in the process of driving a variety of driving behaviors, at the same time, a ...
rating model is built to score drivers' driving behaviors. ...
In the field of video human behavior recognition, there are mainly two mainstream methods: 3D-CNN [7] and Two-Stream Convolutional Networks [8] . ①3D-CNN: the traditional Convolutional Neural Network ...
doi:10.1155/2022/6793365
fatcat:5lnomm4lnzgbzpgvpvkolk34bq
Pedestrian Attributes and Activity Recognition Using Deep Learning: A Comprehensive Survey
2023
Al-Iraqia Journal of Scientific Engineering Research
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Owing to the inadequate image or frame quality of inexpensive cameras and the absence of obvious and stable feature information, direction of pedestrian movement, and so on, the complication of pedestrian ...
This paper is the first of its kind because it combines the recognition of pedestrian attributes and activities separately, and reviews the works on pedestrian attributes and activities using deep learning ...
Zalluhoglu and Ikizler-Cinbis [43] introduced a multi-stream architecture for collective activity recognition based on person regions. ...
doi:10.33193/ijser.2.1.2022.51
fatcat:oeozzb6mrbb6rkpxed6n3jukle
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