Allowance of Driving Based on Drowsiness Detection Using Audio and Video Processing

Sathesh, S.; Maheswaran, S.; Mohanavenkatesan, P.; Azarudeen, M. Mohammed; Sowmitha, K.; Subash, S.

doi:10.1007/978-3-031-16364-7_18

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…Using ST data extracted from 3D-CNN, st ∈ 𝑆𝑆𝑆𝑆 𝑊𝑊𝑊𝑊 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 * 𝐻𝐻𝐻𝐻 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 * 𝐷𝐷𝐷𝐷 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 and predicted state conditions of sub models Ȏ, fusion model identifies the collection of adaptive-Conditional feature representation γ. This γ vector is calculated by multiplicative interaction approach [9,[17][18][19]. The highly dependent and relevant features are identified by multiplicative interaction among the feature maps (element-wise).…”

Section: Feature Fusion Phasementioning

confidence: 99%

Face Positioned Driver Drowsiness Detection Using Multistage Adaptive 3D Convolutional Neural Network

Adhithyaa,

Tamilarasi,

Sivabalaselvamani

et al. 2023

ITC

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Accidents due to driver drowsiness are observed to be increasing at an alarming rate across all countries and it becomes necessary to identify driver drowsiness to reduce accident rates. Researchers handled many machine learning and deep learning techniques especially many CNN variants created for drowsiness detection, but it is dangerous to use in real time, as the design fails due to high computational complexity, low evaluation accuracies and low reliability. In this article, we introduce a multistage adaptive 3D-CNN model with multi-expressive features for Driver Drowsiness Detection (DDD) with special attention to system complexity and performance. The proposed architecture is divided into five cascaded stages: (1) A three level Convolutional Neural Network (CNN) for driver face positioning (2) 3D-CNN based Spatio-Temporal (ST) Learning to extract 3D features from face positioned stacked samples. (3) State Understanding (SU) to train 3D-CNN based drowsiness models (4) Feature fusion using ST and SU stages (5) Drowsiness Detection stage. The Proposed system extract ST values from the face positioned images and then merges it with SU results from each state understanding sub models to create conditional driver facial features for final Drowsiness Detection (DD) model. Final DD Model is trained offline and implemented in online, results show the developed model performs well when compared to others and additionally capable of handling Indian conditions. This method is applied (Trained and Evaluated) using two different datasets, Kongu Engineering College Driver Drowsiness Detection (KEC-DDD) own dataset and National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) Benchmark Dataset. The proposed system trained with KEC-DDD dataset produces accuracy of 77.45% and 75.91% using evaluation set of KEC-DDD and NTHU-DDD dataset and capable to detect driver drowsiness from 256×256 resolution images at 39.6 fps at an average of 400 execution seconds.

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Section: Feature Fusion Phasementioning

confidence: 99%

Face Positioned Driver Drowsiness Detection Using Multistage Adaptive 3D Convolutional Neural Network

Adhithyaa,

Tamilarasi,

Sivabalaselvamani

et al. 2023

ITC

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“…A fundamental aspect of sound processing and analysis is detecting sound events, which has several applications, such as in security, medicine, and monitoring of urban events, and can be used simultaneously with information acquired by security and traffic cameras to increase detection accuracy and coverage. For example, in security systems, namely, in situations where an imaging camera cannot fully acquire the scene of an event for some reason, sound signals can be used in parallel to increase the accuracy and efficiency of the event detection system [3,4]. In most sound event recognition systems based on DL, researchers have attempted to improve their efficiency and accuracy using standard sound features and modifying the structure of the used DL network.…”

Section: Introductionmentioning

confidence: 99%

Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

Hajihashemi,

Alavigharahbagh,

Machado

et al. 2024

Multimed Tools Appl

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The detection of sound events has become increasingly important due to the development of signal processing methods, social media, and the need for automatic labeling methods in applications such as smart cities, navigation, and security systems. For example, in such applications, it is often important to detect sound events at different levels, such as the presence or absence of an event in the segment, or to specify the beginning and end of the sound event and its duration. This study proposes a method to reduce the feature dimensions of a Sound Event Detection (SED) system while maintaining the system’s efficiency. The proposed method, using Empirical Mode Decomposition (EMD), Intrinsic Mode Functions (IMFs), and extraction of locally regulated features from different IMFs of the signal, shows a promising performance relative to the conventional features of SED systems. In addition, the feature dimensions of the proposed method are much smaller than those of conventional methods. To prove the effectiveness of the proposed features in SED tasks, two segment-based approaches for event detection and sound activity detection were implemented using the suggested features, and their effectiveness was confirmed. Simulation results on the URBAN SED dataset showed that the proposed approach reduces the number of input features by more than 99% compared with state-of-the-art methods while maintaining accuracy. According to the obtained results, the proposed method is quite promising.

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“…Overall, efforts to improve driver safety and lower the risks of fatigue-related accidents on the road are being made in the area of detecting driver tiredness during driving events. Sathesh et al (2022) included a way to tell whether a person is feeling sleepy before they start driving. This approach uses a system where the driver receives permission to drive.…”

Section: Introductionmentioning

confidence: 99%

Data-driven approach for effective fatigue detection in drivers during driving events

Vyas,

Tyagi,

Sharma

2023

Multidiscip. Sci. J.

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The effectiveness of transportation and the safety of the road depend heavily on drivers during driving events. The way people drive, and the likelihood of accidents or other occurrences may both be strongly impacted by their actions and behaviors. Promoting safe and sensible driving behaviors requires an understanding of the traits and variables that affect drivers during driving incidents. The rising number of traffic accidents highlights the critical necessity to rein in and lessen the prevalence of careless driving. One of the most common causes of these serious mistakes is drowsiness while driving. To combat this problem, algorithms have been created to identify signs of driver weariness and sound an alarm. The developed algorithms have a serious flaw in their accuracy, and it also takes too long to identify driver drowsiness before alerting them. Timeliness and precision are two of the most important factors in preventing traffic mishaps. Multiple datasets have been utilized to improve methods for identifying signs of exhaustion or drowsiness. These data were acquired either through video streaming records of the driver's behavior or from the driver's brain electroencephalogram (EEG) readings. In order to create a high-performance fatigue detection system, this research designs a novel firefly-integrated optimum cascaded convolutional neural network (FI-OCCNN). The suggested approach offers the greatest detection accuracy among the existing classifiers, up to 98.75%. The studies further show that the recommended methods provide the greatest level of detecting precision with the quickest testing time (TT) compared to all other existing and successful tiredness detection techniques.

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Allowance of Driving Based on Drowsiness Detection Using Audio and Video Processing

Cited by 11 publications

References 22 publications

Face Positioned Driver Drowsiness Detection Using Multistage Adaptive 3D Convolutional Neural Network

Face Positioned Driver Drowsiness Detection Using Multistage Adaptive 3D Convolutional Neural Network

Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

Data-driven approach for effective fatigue detection in drivers during driving events

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