Comparison of face detection and image classification for detecting front seat passengers in vehicles

Artan, Yusuf; Paul, Peter V.; Perronin, Florent; Burry, Aaron

doi:10.1109/wacv.2014.6835994

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…9. In line with earlier studies [3,12], FV typically performs better in terms of accuracy than VLAD and BoW features. shows the misses and false alarms obtained with this approach, respectively.…”

Section: Classification From Full Face Imagessupporting

confidence: 88%

“…Among these transportation imaging systems, the cameras installed to manage High Occupancy Vehicle (HOV) and High Occupancy Tolling (HOT) lanes provides an opportunity for driver cell phone violation detection as these cameras have NIR capabilities to enable night vision and are directed towards the front windshield of a vehicle to estimate the vehicle occupancy from captured images. Several image-based vehicle occupancy detection systems have been examined in the literature [10,7,3] to estimate passenger occupancy from HOV/HOT images.…”

Section: Introductionmentioning

confidence: 99%

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Driver Cell Phone Usage Detection from HOV/HOT NIR Images

Artan

Bulan

Loce

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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Distracted driving due to cell phone usage is an increasingly costly problem in terms of lost lives and damaged property. Motivated by its impact on public safety and property, several state and federal governments have enacted regulations that prohibit driver mobile phone usage while driving. These regulations have created a need for cell phone usage detection for law enforcement. In this paper, we propose a computer vision based method for determining driver cell phone usage using a near infrared (NIR) camera system directed at the vehicle's front windshield. The developed method consists of two stages; first, we localize the driver's face region within the front windshield image using the deformable part model (DPM). Next, we utilize a local aggregation based image classification technique to classify a region of interest (ROI) around the drivers face to detect the cell phone usage. We propose two classification architectures by using full face and half face images for classification and compare their performance in terms of accuracy, specificity, and sensitivity. We also present a comparison of various local aggregation-based image classification methods using bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD) and Fisher vectors (FV). A data set of 1500 images was collected on a public roadway and is used to perform the experiments.

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Section: Classification From Full Face Imagessupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Driver Cell Phone Usage Detection from HOV/HOT NIR Images

Artan

Bulan

Loce

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops

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“…To solve this problem, various studies were conducted to automate the vehicle occupant estimate process. e research can be divided into two detection technology areas: using in-vehicle sensors [4][5][6][7][8][9][10] and using the image data from outside cameras [11][12][13][14][15][16][17]. When using in-vehicle sensors, the accuracy is generally high; however, all vehicles need to be equipped with devices that can detect the number of passengers.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, most studies that detect occupants using outside cameras had limited scope. For example, they can only detect the number of passengers in the front seat [12][13][14], only count the number of children onboard [16], or only determine if two or more passengers have boarded a vehicle. In particular, in [17], an 88% detection accuracy was achieved using image data captured outside the vehicle by one front and one side camera.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method

Lee

Byun

Lim³

et al. 2020

Journal of Advanced Transportation

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High-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some companies have tried to develop an automated detection system. Due to the difficulties of the image treatment process, those systems had limitations. This study overcomes these limits and proposes an overall framework for an algorithm that effectively detects occupants in vehicles using photographic data. Particularly, we apply a new data labeling method that enables highly accurate occupant detection even with a small amount of data. The new labeling method directly labels the number of occupants instead of performing face or human labeling. The human labeling, used in existing research, and occupant labeling, this study suggested, are compared to verify the contribution of this labeling method. As a result, the presented model’s detection accuracy is 99% for the binary case (2 or 3 occupants or not) and 91% for the counting case (the exact number of occupants), which is higher than the previously studied models’ accuracy. Basically, this system is developed for the two-sided camera, left and right, but only a single side, right, can detect the occupancy. The single side image accuracy is 99% for the binary case and 87% for the counting case. These rates of detection are also better than existing labeling.

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“…smartcard and/or transponder technologies(Schijns, 2004;Wood and Jones-Meyer, 2016) 5 infrared-laser-based detection and verification attempts(Artan et al, 2014(Artan et al, , 2016Ballantyne, 2006;Kalikova et al, 2015;Morris et al, 2017;Tyrer and Andrew, …”

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confidence: 99%

Carpooling and the Pan-European emergency call "eCall 112": Connected cars and their potential for environmental and transport policy

Schaefer¹

2019

IJATM

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To meet transport sector obligations to emissions reductions, large effects may be derived from intelligent transportation systems (ITS) solutions. Substantial decrease of vehicle miles travelled (VMT) can be achieved using transportation systems management (TSM) measures, especially through high-occupancy vehicles (HOV) incentives. Of these, carpooling and vanpooling approaches proved to be effective. However, there is a lack of reliable enforcement procedures. A review of automated vehicle occupancy verification (AVOV) research and development (R&D) is followed by a proposal focused on the Pan European emergency call 'eCall 112'. The concept explores options for an eCall 112 application as an enforcement tool in TSM operations with vehicle occupancy data contained in the minimum set of data (MSD) as the base for new road transport policy tools. A set of incentives is described which support transport and environmental objectives. The proposed enforcement may impact congestion levels in combination with HOV-lanes and without them. Climate, environment, and the performance of road transportation systems could benefit from integrated road safety and TSM operations.

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Comparison of face detection and image classification for detecting front seat passengers in vehicles

Cited by 10 publications

References 8 publications

Driver Cell Phone Usage Detection from HOV/HOT NIR Images

Driver Cell Phone Usage Detection from HOV/HOT NIR Images

A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method

Carpooling and the Pan-European emergency call "eCall 112": Connected cars and their potential for environmental and transport policy

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