Driver Pose Estimation Using Recurrent Lightweight Network and Virtual Data Augmented Transfer Learning

Liu, Yazhou; Lasang, Pongsak; Pranata, Sugiri; Shen, Shengmei; Zhang, Wenchao

doi:10.1109/tits.2019.2921325

Cited by 22 publications

(5 citation statements)

References 54 publications

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“…In some studies, distracted driving has been detected via non-vehicle-based devices, or frameworks with low computational resources have been developed. In other studies, the number of model parameters has been reduced by optimizing the structure of deep learning models [26,27]. As knowledge distillation has become an important lightweighting tool for deep learning models, it has been applied to the field of driver distraction monitoring [28,29].…”

Section: B Deep Learning Feature Extraction and Classificationmentioning

confidence: 99%

Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer

Shi

2024

IEEE Access

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Section: B Deep Learning Feature Extraction and Classificationmentioning

confidence: 99%

Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer

Shi

2024

IEEE Access

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“…e study by Bin et al [29] showed that abnormal behaviour of the human body is detected based on the human pose, detects the pose of human behaviour in different scenes, and judges whether human behaviour is in the abnormal state. In the related field of safe driving, the research on driver attitude has become one of the most important research directions of ADAS [30]. Dua et al [31] consider that some drivers covering their mouth with hands would lead to wrong judgment of yawning.…”

Section: Related Workmentioning

confidence: 99%

Driver Fatigue Detection Method Based on Human Pose Information Entropy

Zhang

et al. 2022

Journal of Advanced Transportation

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Driver fatigue detection (DFD) is an effective method to prevent traffic accidents. The existing research on DFD using facial features is an effective and noninvasive fatigue detection method. However, this approach is affected by facial occlusions (glasses, sunglasses, masks, etc.) and the large facial pose deformations in the extraction of effective fatigue features. In this paper, we introduce a novel DFD method using human pose information entropy. The method first estimates human pose from video sequences and then uses them as clues to extract multiple fatigue-related features which can reduce the influence of facial occlusion and head pose deformation. Information entropy and sliding window algorithm are applied to analyse and calculate sufficient consecutive video frames to obtain more robust and accurate fatigue-related values than by using a single frame. These information entropy values are combined resorting to the support vector machine (SVM) to recognize the driver fatigue state. Experimental results show that the method can achieve much higher accuracy and robustness, and the detection speed meets the requirements of real time.

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“…Driver gesture recognition is a key component of advanced diver assistance systems, which has potential value in applications such as autonomous driving, driver behavior understanding, human-computer interaction, and driver attention analysis. Liu et al [180] designed a novel network RM-ThinNet, which uses a lightweight model to estimate the driver's posture. Extensive experimental results also demonstrate the effectiveness of the proposed model.…”

Section: ) Intelligent Transportation Systems (Its)mentioning

confidence: 99%

Compacting Deep Neural Networks for Internet of Things: Methods and Applications

Zhang

Ying

Dai

et al. 2021

IEEE Internet Things J.

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Deep Neural Networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this paper presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression, 2) Knowledge Distillation (KD), 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future directions.

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Driver Pose Estimation Using Recurrent Lightweight Network and Virtual Data Augmented Transfer Learning

Cited by 22 publications

References 54 publications

Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer

Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer

Driver Fatigue Detection Method Based on Human Pose Information Entropy

Compacting Deep Neural Networks for Internet of Things: Methods and Applications

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