Driver-Condition Detection Using a Thermal Imaging Camera and Neural Networks

Kajiwara, Shinji

doi:10.1007/s12239-021-0130-3

Cited by 11 publications

(2 citation statements)

References 20 publications

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“…Moreover, the market entry of smaller and low-cost thermal cameras is paving the way for thermal IR imaging applications outside laboratory environments, especially for cutting-edge applications in the affective computing field. Furthermore, smart thermal devices have proved effective in important real-life scenarios, such as driver drowsiness state evaluation [73,74], human stress recognition [75], and smartphone-based clinical imaging systems [76,77]. In this regard, this research reports a significant novelty.…”

Section: Discussionmentioning

confidence: 99%

Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach

Filippini

Crosta

Palumbo

et al. 2022

Sensors

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Extensive possibilities of applications have rendered emotion recognition ineluctable and challenging in the fields of computer science as well as in human-machine interaction and affective computing. Fields that, in turn, are increasingly requiring real-time applications or interactions in everyday life scenarios. However, while extremely desirable, an accurate and automated emotion classification approach remains a challenging issue. To this end, this study presents an automated emotion recognition model based on easily accessible physiological signals and deep learning (DL) approaches. As a DL algorithm, a Feedforward Neural Network was employed in this study. The network outcome was further compared with canonical machine learning algorithms such as random forest (RF). The developed DL model relied on the combined use of wearables and contactless technologies, such as thermal infrared imaging. Such a model is able to classify the emotional state into four classes, derived from the linear combination of valence and arousal (referring to the circumplex model of affect’s four-quadrant structure) with an overall accuracy of 70% outperforming the 66% accuracy reached by the RF model. Considering the ecological and agile nature of the technique used the proposed model could lead to innovative applications in the affective computing field.

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Section: Discussionmentioning

confidence: 99%

Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach

Filippini

Crosta

Palumbo

et al. 2022

Sensors

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“…However, several uncontrollable elements, such as weather, traffic, etc., could also cause irregular driving patterns and not necessarily signify dangerous driving. Vision-based approaches: Vision-based approaches [20], [21] leverage computer vision on RGB camera [22], thermal imaging [23], and Infrared (IR) camera [24] to detect abnormal driving activities by directly focusing on the driver. The captured images within the environment are processed to determine movements, including facial features such as eye movements, talking, and yawning, as well as movements of other body parts [25] such as head movements, hand movements, etc.…”

Section: Related Workmentioning

confidence: 99%

mmDrive: mmWave Sensing for Live Monitoring and On-Device Inference of Dangerous Driving

Sen¹,

Mandal²,

Karmakar³

et al. 2023

Preprint

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Detecting dangerous driving has been of critical interest for the past few years. However, a practical yet minimally intrusive solution remains challenging as existing technologies heavily rely on visual features or physical proximity. With this motivation, we explore the feasibility of purely using mmWave radars to detect dangerous driving behaviors. We first study characteristics of dangerous driving and find some unique patterns of range-doppler caused by 9 typical dangerous driving actions. We then develop a novel Fused-CNN model to detect dangerous driving instances from regular driving and classify 9 different dangerous driving actions. Through extensive experiments with 5 volunteer drivers in real driving environments, we observe that our system can distinguish dangerous driving actions with an average accuracy of 97(±2)%. We also compare our models with existing state-of-the-art baselines to establish their significance.

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FMDCDTL: Design of Fusion-Based Model for Identification of Drowsy Drivers via Cascaded Deep Transfer Learning

Wakode¹,

Mohod²

2022

Lecture Notes in Networks and Systems

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Driver-Condition Detection Using a Thermal Imaging Camera and Neural Networks

Cited by 11 publications

References 20 publications

Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach

Automated Affective Computing Based on Bio-Signals Analysis and Deep Learning Approach

mmDrive: mmWave Sensing for Live Monitoring and On-Device Inference of Dangerous Driving

FMDCDTL: Design of Fusion-Based Model for Identification of Drowsy Drivers via Cascaded Deep Transfer Learning

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