In-Vehicle Violence Detection in Carpooling: A Brief Survey Towards a General Surveillance System

Marcondes, Francisco Supino; Durães, Dalila; Gonçalves, Filipe; Fonseca, Joaquim; Machado, José; Nováis, Paulo

doi:10.1007/978-3-030-53036-5_23

Cited by 11 publications

(7 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The aim of this study was to implement a similar approach in FAVs. In-vehicle violence detection is reviewed in [12]. Authors have surveyed multiple articles and AI methods for in-vehicle emotion detection to develop violence detection in carpooling services.…”

Section: In-cabin Monitoringmentioning

confidence: 99%

“…Sensors 2022, 22, x FOR PEER REVIEW 4 of 22 implement a similar approach in FAVs. In-vehicle violence detection is reviewed in [12].…”

Section: Safety and Security In Level 4 And Beyond Avsmentioning

confidence: 99%

“…To enhance the safety and security inside the vehicle's cabin, multiple works have been reported [5][6][7][8][9][10][11][12][13][14][15][16][17]. Recently various companies have also reported their in-cabin monitoring systems for improving the safety and security of the occupants inside the vehicle [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

In-Cabin Monitoring System for Autonomous Vehicles

Mishra

Lee

Kim

et al. 2022

Sensors

View full text Add to dashboard Cite

In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera. We have investigated the issues for mobility services in higher levels of autonomous driving, what needs to be monitored, how to monitor, etc. Our proposed IMS is an on-device AI system that indigenously has improved the privacy of the users. Furthermore, we have enlisted the essential actions to be considered in an IMS and developed an appropriate database (DB). Our DB consists of multifaced scenarios important for monitoring the in-cabin of the higher-level AVs. Moreover, we have compared popular AI models applied for object and occupant recognition. In addition, our DB is available on request to support the research on the development of seamless monitoring of the in-cabin higher levels of autonomous driving for the assurance of safety and security.

show abstract

Section: In-cabin Monitoringmentioning

confidence: 99%

“…Sensors 2022, 22, x FOR PEER REVIEW 4 of 22 implement a similar approach in FAVs. In-vehicle violence detection is reviewed in [12].…”

Section: Safety and Security In Level 4 And Beyond Avsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

In-Cabin Monitoring System for Autonomous Vehicles

Mishra

Lee

Kim

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Violence recognition is a sub-area of human action recognition that can be divided between internal and external environments [1]. This is a crucial distinction, as the issues and problems to be addressed in these two types of environments are quite different.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the hardware should not be expensive enough to be worth stealing. This imposes several hardware constraints that can be summarized as surveillance must be optimized for as little computational complexity as possible [1].…”

Section: Introductionmentioning

confidence: 99%

Weakness Evaluation on In-Vehicle Violence Detection: An Assessment of X3D, C2D and I3D against FGSM and PGD

et al. 2022

Self Cite

View full text Add to dashboard Cite

When constructing a deep learning model for recognizing violence inside a vehicle, it is crucial to consider several aspects. One aspect is the computational limitations, and the other is the deep learning model architecture chosen. Nevertheless, to choose the best deep learning model, it is necessary to test and evaluate the model against adversarial attacks. This paper presented three different architecture models for violence recognition inside a vehicle. These model architectures were evaluated based on adversarial attacks and interpretability methods. An analysis of the model’s convergence was conducted, followed by adversarial robustness for each model and a sanity-check based on interpretability analysis. It compared a standard evaluation for training and testing data samples with the adversarial attacks techniques. These two levels of analysis are essential to verify model weakness and sensibility regarding the complete video and in a frame-by-frame way.

show abstract