In-Vehicle Occupancy Detection With Convolutional Networks on Thermal Images

Nowruzi, Farzan Erlik; Ahmar, Wassim A. El; Laganière, Robert; Ghods, Amir H.

doi:10.1109/cvprw.2019.00124

Cited by 26 publications

(12 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To accurately estimate how many seats are occupied and further localize and recognize the objects 1 , image-based approaches are extensively studied [19]- [21], [164], [177], [178] because an image can provide more visible information such as the contour/edge of an object than WiFi signals. By leveraging techniques such as edge detection [19]- [21], and learning including convolutional neural network (CNN), multi-task learning [164], which can automatically identify object-related features for recognition, great performance can be achieved.…”

Section: Image-based Occupancy Detectionmentioning

confidence: 99%

In-Vehicle Sensing for Smart Cars

Zeng

Wang

Wang³

et al. 2022

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

Section: Image-based Occupancy Detectionmentioning

confidence: 99%

In-Vehicle Sensing for Smart Cars

Zeng

Wang

Wang³

et al. 2022

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

“…10 Additionally, camera sensors invade the privacy of users. Thermal cameras can also be used for occupancy detection 11 but only achieve a detection accuracy of 91%. This level of accuracy is not sufficient as any missed detection in the case of children left behind can be fatal.…”

Section: Introductionmentioning

confidence: 99%

Improving passenger safety in cars using novel radar signal processing

Abedi

Magnier

Mazumdar

et al. 2021

Engineering Reports

View full text Add to dashboard Cite

According to the group Kids and Cars, since 1990, nearly 1000 kids lost their lives because they were deliberately or unintentionally left in parked vehicles to potentially overheat or freeze. The development of technology able to prevent and address this serious, worldwide problem is crucial. In this paper, we deploy a radar-based sensor for in-vehicle presence-absence detection of a living body. We present a novel radar signal processing technique to identify the presence or absence of a living body in a vehicle using a mm-wave frequency-modulated continuous-wave (FMCW) radar. Our proposed method is based on reflections from breathing cycles creating correlated and consistent micro-Doppler effects over time. The performance of the system is evaluated with adults and two phantoms mimicking the breathing of children in various scenarios. The results show that we can clearly detect any tiny living body in vehicles with 100% accuracy without a need for any compute-intensive complex signal processing, making the system of extreme low-cost. The results demonstrate the high sensitivity and robustness of the mm-wave system in extensive studies over the course of multiple months.

show abstract

“…car [8]. To solve this problem, a thermal camera-based method was proposed in [9] to detect seat occupancy inside a vehicle. The thermal image was used as an input to the convolutional neural network and the number of people was estimated with a high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network-Based In-Vehicle People Localization Using Ultra-Wideband Radar

et al. 2020

View full text Add to dashboard Cite

In this paper, we present an in-vehicle people localization technique using a deep neural network (DNN) model that is trained by the experimental data. First, an impulse radio ultra-wide band (IR-UWB) radar is installed inside the vehicle, and received signals are acquired by changing the arrangement of people sitting. Then, on the acquired data, we apply the DNN to train a classifier, which can predict whether a person is sitting or not in each seat. To design a network suitable for our system, we evaluate the performance by changing the type of activation function, the number of layers, and the number of nodes in each hidden layer of the DNN. In addition, we compare the performance of the proposed method with conventional machine learning algorithms such as support vector machine (SVM) and decision tree-based methods. From our measured signals, the proposed DNN-based method can classify all possible cases according to the location and number of people with an accuracy of 99 %. Moreover, the advantage of our proposed method is that there is no need to extract features from a given radar signal. INDEX TERMS Deep neural network, impulse radio ultra-wide band (IR-UWB) radar, people localization.

show abstract

In-Vehicle Occupancy Detection With Convolutional Networks on Thermal Images

Cited by 26 publications

References 16 publications

In-Vehicle Sensing for Smart Cars

In-Vehicle Sensing for Smart Cars

Improving passenger safety in cars using novel radar signal processing

Deep Neural Network-Based In-Vehicle People Localization Using Ultra-Wideband Radar

Contact Info

Product

Resources

About