A Novel Spatial-Temporal Graph for Skeleton-based Driver Action Recognition

Li, Peng; Lu, Meiqi; Zhang, Zhiwei; Shan, Donghui; Yang, Yang

doi:10.1109/itsc.2019.8916929

Cited by 24 publications

(5 citation statements)

References 12 publications

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“…Drawing inspiration from the robust methods in the paper "Robust Construction of Spatial-Temporal Scene Graph Considering Perception Failures for Autonomous Driving" [39], the metrics utilized in our quantitative evaluation consider potential perception failures. These evaluation metrics offer a compelling means to assess the effectiveness and accuracy of our scenario-based segmentation model, thus ensuring the model's robustness in autonomous driving contexts.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s Scenario

Nham,

Lee,

Yang

et al. 2024

IEEE Access

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This paper introduces the Scenario-Based Segmentation Network (SBS-Net), which highlights significant advances in autonomous driving. Through the integration of the Scenario Enhanced Graph Neural Network (SE-GNN) and graph re-match modules into the existing semantic segmentation network model based on driver's cognition (DCSeg-Net), our approach optimizes the graph construction process. The SE-GNN module enhances the extraction of critical features and relations within diverse driving scenarios, elevating both accuracy and the system's adaptability to complex driving scenarios. The introduced graph rematch module refines classification discrepancies, significantly boosting segmentation accuracy and refining the understanding of autonomous driving scenes. Beyond technological enhancements, this work outlines the expansion and diversification of our original dataset, strengthening the learning capabilities of our model by including specific classification labels and incorporating a broader range of driving scenarios. The utility of the improved SBS-Net is demonstrated through superior performance in Graph Construction Accuracy and Intersection over Union (IoU) measures, as highlighted in our evaluation metrics. These advances underscore the practical applicability of scenariobased segmentation in real-world autonomous driving scenarios, enhancing overall scene comprehension capabilities. The developments presented signal substantial progress in the field of autonomous driving technology.

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Section: Quantitative Evaluationmentioning

confidence: 99%

Scenario-Based Segmentation: Traffic Image Segmentation by GNN Based Driver’s Scenario

Nham,

Lee,

Yang

et al. 2024

IEEE Access

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“…Check for details. StateFarm [ 192 ] Safe Drive, Text-Right, Talk on the Phone-Right, Text-Left, Talk on the Phone-Left, Operate the Radio, Drink, Reach Behind, Hair and Makeup, Talk to Passenger DHG-14/28 [ 70 ] Grab, Tap, Expand, Pinch, Rotate Clockwise, Rotatr Couter Clockwise, Swipe Right, Swipe Left, Swipe Up, Swipe Down, Swipe X, Swipe V, Swipe +, Shake Volleyball [ 115 ] Wait, Set, Dig, Fall, Spike, Block, Jump, Move, Stand SYSU [ 62 , 69 , 72 , 75 , 85 , 90 , 97 , 135 , 136 ] Drink, Pour, Call Phone, Play Phone, Wear Backpacks, Pack Backpacks, Sit Chair, Move Chair, Take Out Wallet, Take From Wallet, Mope, Sweep SHREC’17 [ 70 ] Grab, Tap, Expand, Pinch, Rotate Clockwise, Rotatr Couter Clockwise, Swipe Right, Swipe Left, Swipe Up, Swipe Down, Swipe X, Swipe V, Swipe + and Shake Kinetics [ 13 , 14 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 57 , 60 , …”

Section: Table A1mentioning

confidence: 99%

Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey

Feng

Meunier

2022

Sensors

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Human action recognition has been applied in many fields, such as video surveillance and human computer interaction, where it helps to improve performance. Numerous reviews of the literature have been done, but rarely have these reviews concentrated on skeleton-graph-based approaches. Connecting the skeleton joints as in the physical appearance can naturally generate a graph. This paper provides an up-to-date review for readers on skeleton graph-neural-network-based human action recognition. After analyzing previous related studies, a new taxonomy for skeleton-GNN-based methods is proposed according to their designs, and their merits and demerits are analyzed. In addition, the datasets and codes are discussed. Finally, future research directions are suggested.

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“…Until recently, the majority of driving observation frameworks comprised a manual feature extraction step followed by a classification module (for a thorough overview see [21]). The constructed feature vectors are often derived from hand-and body-pose [2], [3], [6], [7], [38], [39], facial expressions and eye-based input [40], [41], and head pose [42], [43], but also foot dynamics [44], detected objects [6], and physiological signals [45] have been considered. Classification approaches are fairly similar to the ones used in standard video classification, with LSTMs [3], [4], SVMs [2], [46], random forests [47] or HMMs [4], and graph neural networks [7], [48] being popular choices.…”

Section: Related Work a Driver Action Recognitionmentioning

confidence: 99%