Driver Cell Phone Usage Detection from HOV/HOT NIR Images

Artan, Yusuf; Bulan, Orhan; Loce, Robert P.; Paul, Peter V.

doi:10.1109/cvprw.2014.42

Cited by 51 publications

(24 citation statements)

References 7 publications

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“…As can be seen from the table, HOG features provide for a more robust representation and result in higher classification accuracy rates, Area Under the Curve (AUC) values, and higher Verification Rates (VRs) at various False Accept Rates (FARs) for all three classifiers with the combination of AdaBoost and HOG features resulting in the highest classification rate of 93.86%. Thus, our results are promising and competitive with those obtained in similar studies carried out by Artan et al [4] (highest classification rate of 86.19%) and Zhang et al [29] (highest classification rate of 91.20%), although it must be noted that each study utilized different training and testing data. However, our study is far more thorough than the previously mentioned ones in that our tests are carried out over a much larger set of images and also in the choice of data used for evaluation, which was acquired using strict protocols by a government agency for a specific purpose.…”

Section: Experiments and Resultssupporting

confidence: 84%

“…Thus, the total number of test frames was 13023, making our study more comprehensive than the one carried out in [4]. The subject held a cell phone in his/her left hand in only 429 frames out of the 3735 frames in which a cell phone was being used.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Artan et al [4] used data captured by a highway transportation imaging system, which was installed to manage High Occupancy Vehicle (HOV) and High Occupancy Tolling (HOT) lanes, for detecting cell phone usage by drivers. The cameras used were situated at an elevated position pointing towards the approaching traffic with Near Infrared (NIR) capability to tackle night vision.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Driver cell phone usage detection on Strategic Highway Research Program (SHRP2) face view videos

Seshadri

Juefei-Xu

Pal

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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The harmful effects of cell phone usage on driver behavior have been well investigated and the growing problem has motivated several several research efforts aimed at developing automated cell phone usage detection systems. Computer vision based approaches for dealing with this problem have only emerged in recent years. In this paper, we present a vision based method to automatically determine if a driver is holding a cell phone close to one of his/her ears (thus keeping only one hand on the steering wheel) and quantitatively demonstrate the method's efficacy on challenging Strategic Highway Research Program (SHRP2) face view videos from the head pose validation data that was acquired to monitor driver head pose variation under naturalistic driving conditions. To the best of our knowledge, this is the first such evaluation carried out using this relatively new data. Our approach utilizes the Supervised Descent Method (SDM) based facial landmark tracking algorithm to track the locations of facial landmarks in order to extract a crop of the region of interest. Following this, features are extracted from the crop and are classified using previously trained classifiers in order to determine if a driver is holding a cell phone. We adopt a through approach and benchmark the performance obtained using raw pixels and Histogram of Oriented Gradients (HOG) features in combination with various classifiers.

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Section: Experiments and Resultssupporting

confidence: 84%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Driver cell phone usage detection on Strategic Highway Research Program (SHRP2) face view videos

Seshadri

Juefei-Xu

Pal

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…In connection with secondary tasks recognition, different computer vision algorithms have been proposed in order to detect cell phone usage of the driver while driving [96,97,98,99,100]. High recognition rates are usually obtained (from 86.19% to 95%) using very different approaches.…”

Section: Biomechanical Distractionmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

101

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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“…Approaches have been developed to register relatively simple variables, such as traffic flow and density [1], [2], speed [3]- [5], traffic light violations [6], or collisions [7]. More recently, computer vision has been used to register more complex road user behaviors, such as driver mobile phone use [8] and unauthorized use of car-pooling lanes [9]. Since for many developing countries the main form of motorized transport consists of motorcycles, the detection of motorcycle helmet use of riders through machine learning has also been explored [10] [11].…”

Section: Introductionmentioning

confidence: 99%

Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning

2020

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Automated detection of motorcycle helmet use through video surveillance can facilitate efficient education and enforcement campaigns that increase road safety. However, existing detection approaches have a number of shortcomings, such as the inabilities to track individual motorcycles through multiple frames, or to distinguish drivers from passengers in helmet use. Furthermore, datasets used to develop approaches are limited in terms of traffic environments and traffic density variations. In this paper, we propose a CNN-based multi-task learning (MTL) method for identifying and tracking individual motorcycles, and register rider specific helmet use. We further release the HELMET dataset, which includes 91,000 annotated frames of 10,006 individual motorcycles from 12 observation sites in Myanmar. Along with the dataset, we introduce an evaluation metric for helmet use and rider detection accuracy, which can be used as a benchmark for evaluating future detection approaches. We show that the use of MTL for concurrent visual similarity learning and helmet use classification improves the efficiency of our approach compared to earlier studies, allowing a processing speed of more than 8 FPS on consumer hardware, and a weighted average F-measure of 67.3% for detecting the number of riders and helmet use of tracked motorcycles. Our work demonstrates the capability of deep learning as a highly accurate and resource efficient approach to collect critical road safety related data.

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Driver Cell Phone Usage Detection from HOV/HOT NIR Images

Cited by 51 publications

References 7 publications

Driver cell phone usage detection on Strategic Highway Research Program (SHRP2) face view videos

Driver cell phone usage detection on Strategic Highway Research Program (SHRP2) face view videos

Driver Distraction Using Visual-Based Sensors and Algorithms

Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning

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