In-Vehicle Occupancy Detection And Classification Using Machine Learning

Vamsi, Malneedi; Soman, K P

doi:10.1109/icccnt49239.2020.9225661

Cited by 14 publications

(8 citation statements)

References 4 publications

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“…It detected occupants with a general accuracy of 94.5%, those occupants were correctly classified as front-seat passenger with an accuracy of 97.3%, as driver with 99.5% accuracy, and as back-seat passenger with 94.3% accuracy. Vamsi et al [14] detects passengers and classifying each person as a child or adult based on an image from a camera placed inside car. The main, widely used technique of detection is the Haar Cascades for detection.…”

Section: Vision Methods For In Cabin Occupant Monitoringmentioning

confidence: 99%

In-cabin occupant monitoring system based on improved Yolo, deep reinforcement learning, and multi-task CNN for autonomous driving

KHRAIEF OULED AZAIZ,

DACLEU NDENGUE

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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Vehicle in-cabin occupant monitoring system is becoming a crucial feature of the automobile industry and challenging research topic to enhance both vehicle safety, security, and comfort of conventional and future intelligent vehicles. Precise information about the number, position, and characteristics of occupants as well as objects located inside the vehicle must be available. Current industrial systems for seat occupancy detection are based on multiple weight sensors, capacitive sensors, electric field, or ultrasonic sensors. They cannot necessarily make the right distinction in borderline cases. A simple pressure sensor cannot tell whether the weight on the seat comes from a person or an inanimate object. Recently, the Artificial Intelligence (AI) based advanced systems have attracted attention for various fields such as automobile industry. Especially, with the advancement of deep learning that has shown very high classification accuracies compared to hand-crafted features on various computer vision tasks. For the above reasons, we propose a new automatic AI occupant monitoring system based on two cameras installed inside the vehicle. Our goal is to develop an automatic detection and recognition system with high accuracy performance, low computational cost and small weight model. Our system fuses our modified deep convolutional network Yolo model and deep reinforcement learning to detect and classify passengers and objects inside the vehicle. It can predict the gender, the age and the emotion of occupants based on our proposed muti task convolutional neural networks. In our end-to-end system, this approach is more efficient time and memory wise by solving all the tasks in the same process and storing a single CNN instead of storing a CNN for each task. Principal applications of our system are intelligent airbag management, seat belt reminder, life presence and in shared cabin preferences. We perform comparative evaluation based on the public datasets SVIRO, TiCaM, Aff-Wild and Adience dataset to demonstrate the superior performance of our proposed system.

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Section: Vision Methods For In Cabin Occupant Monitoringmentioning

confidence: 99%

In-cabin occupant monitoring system based on improved Yolo, deep reinforcement learning, and multi-task CNN for autonomous driving

KHRAIEF OULED AZAIZ,

DACLEU NDENGUE

2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

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“…A night vision camera connected to the Raspberry Pi board is discussed in the paper [134]. The authors used Haar Cascades as a face detection algorithm based on the easiest feature extraction with high accuracy and less computation time compared to other machine learning algorithms.…”

Section: Passenger Discrimination For Airbag Suppressionmentioning

confidence: 99%

Passenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges

et al. 2023

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Passenger detection and occupancy estimation are vital tasks in many fields. The existing literature emphasises that the increasing demand for such systems will continue to grow. This paper reviews the existing literature specializing in the field of transportation safety and efficiency concerning occupancy estimation in vehicles and passenger detection at public transport stations. A comparison between different approaches to passenger estimation is presented. Discussion on the advantages and disadvantages is highlighted. Hence, this paper provides an analysis of 146 papers on the current state of the field. This review paper concludes that invasive methods provide high accuracy with relatively cheap implementation, while noninvasive systems do not violate passenger privacy but lack state-of-the-art accuracy. Future work will include a systematic literature review and a comparative analysis of systems considering the existing window tinting and solar windshields heavily blocking certain parts of the electromagnetic spectrum. Moreover, future work will investigate the critical challenges of noninvasive passenger estimation in different types of vehicles: trucks, buses, or even motorcycles.

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“…Age classification can be performed using machine learning based on different modalities, including images, speech, or video. Some examples of techniques that have worked on this problem using images for age and gender classification can be found in [27][28][29][30][31], and [32].…”

Section: Age Classificationmentioning

confidence: 99%

LimitAccess: on-device TinyML based robust speech recognition and age classification

Maayah

Abunada

Al-Janahi

et al. 2023

Discov Artif Intell

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Automakers from Honda to Lamborghini are incorporating voice interaction technology into their vehicles to improve the user experience and offer value-added services. Speech recognition systems are a key component of smart cars, enhancing convenience and safety for drivers and passengers. In the future, safety-critical features may rely on speech recognition, but this raises concerns about children accessing such services. To address this issue, the LimitAccess system is proposed, which uses TinyML for age classification and helps parents limit children’s access to critical speech recognition services. This study employs a lite convolutional neural network (CNN) model for two different reasons: First, CNN showed superior accuracy compared to other audio classification models for age classification problems. Second, the lite model will be integrated into a microcontroller to meet its limited resource requirements. To train and evaluate our model, we created a dataset that included child and adult voices of the keyword “open”. The system approach categorizes voices into age groups (child, adult) and then utilizes that categorization to grant access to a car. The robustness of the model was enhanced by adding a new class (recordings) to the dataset, which enabled our system to detect replay and synthetic voice attacks. If an adult voice is detected, access to start the car will be granted. However, if a child’s voice or a recording is detected, the system will display a warning message that educates the child about the dangers and consequences of the improper use of a car. Arduino Nano 33 BLE sensing was our embedded device of choice for integrating our trained, optimized model. Our system achieved an overall F1 score of 87.7% and 85.89% accuracy. LimitAccess detected replay and synthetic voice attacks with an 88% F1 score.

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In-Vehicle Occupancy Detection And Classification Using Machine Learning

Cited by 14 publications

References 4 publications

In-cabin occupant monitoring system based on improved Yolo, deep reinforcement learning, and multi-task CNN for autonomous driving

In-cabin occupant monitoring system based on improved Yolo, deep reinforcement learning, and multi-task CNN for autonomous driving

Passenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges

LimitAccess: on-device TinyML based robust speech recognition and age classification

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