In-Car State Classification with RGB Images

Faria, Pedro; Dixe, Sandra; Leite, João; Azadi, Sahar; Mendes, J.; Fonseca, Jaime C.; Borges, João

doi:10.1007/978-3-030-71187-0_40

Cited by 2 publications

(1 citation statement)

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“…In [ 20 ], Dixe et al presented a monitoring system capable of estimating the state of the car, namely, the presence of damage, dirt, and stains using semantic segmentation. A similar work was developed by Faria et al in [ 21 ] by classifying the state of the vehicle interior with the use of state-of-the-art classifiers using RGB images.…”

Section: Related Workmentioning

confidence: 99%

Fusion Object Detection and Action Recognition to Predict Violent Action

Rodrigues

Costa

Melo

et al. 2023

Sensors

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In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to present a fusion monitoring solution which was three different algorithms: a violent action detection system, which recognizes violent behaviors between passengers, a violent object detection system, and a lost items detection system. Public datasets were used for object detection algorithms (COCO and TAO) to train state-of-the-art algorithms such as YOLOv5. For violent action detection, the MoLa InCar dataset was used to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN, and TSM. Finally, an embedded automotive solution was used to demonstrate that both methods are running in real-time.

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Section: Related Workmentioning

confidence: 99%

Fusion Object Detection and Action Recognition to Predict Violent Action

Rodrigues

Costa

Melo

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

Fusion Object Detection and Action Recognition to Predict Violent Action

Rodrigues¹,

Costa²,

Melo³

et al. 2023

Preprint

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In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to detect objects, namely lost/forgotten items to inform the passengers, and aggressive items to monitoring if violent actions may arise between passengers. For object detection algorithms was used public datasets (COCO and TAO) to train state-of-the-art algorithms, such as YOLOv5. For violent action detection was used the MoLa InCar dataset to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN and TSM. At the end an embedded automotive solution was used to demonstrate both methods running in real-time.

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In-Car State Classification with RGB Images

Cited by 2 publications

References 24 publications

Fusion Object Detection and Action Recognition to Predict Violent Action

Fusion Object Detection and Action Recognition to Predict Violent Action

Fusion Object Detection and Action Recognition to Predict Violent Action

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