driver drowsiness detection using machine learning

Our hypothesis was that as an individual becomes drowsy, they are likely to yawn and lose control over their mouth, making their MAR to be higher than usual in this state. in order to achieve better results. Thank you so much for reading through our entire blog! ILSVRC uses a smaller set of ImageNet with roughly 1000 images in each of 1000 categories. Drowsiness and fatigue lead the cause of road accident in Malaysia. The formula is as below: We chose to use an LSTM network because it allows us to study long sequences without having to worry about the gradient vanishing problems faced by traditional RNNs. �����@\��:����X�҂q���d��AsF��ruU�r4h���]e\��3�4j�!n*O[�wuz`�sˮ;PZĺe7� stream This would trigger your laptop webcam. The binary SVM classifier is used for classification whether the driver is drowsy or not. In order to detect fatigue or drowsiness, small camera has been used which points directly towards the driver’s face and detects the eye ball movement of the driver. MENU. Second, we want to update parameters with our more complex models (NNs, ensembles, etc.) Use the below command to install the dlib. What Do You Think? Driver drowsiness detection system Abstract: Drowsiness and Fatigue of drivers are amongst the significant causes of road accidents. If you loved this story, do join our Telegram Community. endobj The driver is alerted when any of those signs are identified and the driver wakes up. If there eyes have been closed for a certain amount of time, we’ll assume that they are starting to doze off and play an alarm to wake them … It is therefore of great importance to reduce this risky factor by detecting the driver’s drowsiness condition. Problem Statement for building intelligent vehicle systems. There are many facial features that can be extracted from the face to infer the level of drowsiness. Car driver will simulate falling asleep to force a response from the warning system ; The demonstration will wrap up … The way we dealt with this problem was to average the original prediction results with the prediction results from the previous two frames. The idea of using machine-learning methods to detect driver behavior is not completely novel. That is, person A may naturally have much smaller eyes than person B. The approach we will be using for this Python project is … Various studies have suggested that around 20% of all road accidents are fatigue-related, up to 50% on certain roads. The configuration of the fully connected layers is the same in all networks. Team Members: Grant Zhong, Rui Ying, He Wang, Aurangzaib Siddiqui, Gaurav Choudhary, “1 in 25 adult drivers report that they have fallen asleep at the wheel in the past 30 days”. This method can also be extended for aeroplanes and pilots. This led us to the realization that our model was struggling with new faces and the primary reason for this struggle was the fact that each individual has different core features in their default alert state. We tested all eight features in our models and our results improved significantly. Driver Drowsiness Detection System. This implementation combines best of state of the art in machine and deep learning. A possible explanation for this is that images that we passed through the model were of 22 respondents sitting virtually motionless in front of a camera with undisturbed backgrounds. While we hypothesized and tested several features, the four core features that we concluded on for our final models were eye aspect ratio, mouth aspect ratio, pupil circularity, and finally, mouth aspect ratio over eye aspect ratio. Mouth Aspect Ratio after normalization turned out to be the most important feature out of our 8 features. This makes sense because when we are drowsy, we tend to yawn more frequently. Third and finally, we would like to collect our own training data from a larger sample of participants (more data!!!) endobj There are approximately 1.2 million training images, 50,000 validation images, and 150,000 testing images. Our solution to this problem is to build a detection system that identifies key attributes of drowsiness and triggers an alert when someone is drowsy before it is too late. The mean and standard deviation of each feature for these three frames were calculated and used to normalize each feature individually for each participant. If you have driven before, you’ve been drowsy at the wheel at some point. Driver drowsiness detection is a car safety technology which prevents accidents when the driver is getting drowsy. This allowed us to obtain a sufficient amount of data for both the alert and drowsy state. Stark Foundation by 2. Based on this hypothesis, we expected our model to predict the class as drowsy if the eye aspect ratio for an individual over successive frames started to decline i.e. The dataset consists of around 30 hours of videos of 60 unique participants. Previous approaches to drowsiness detection primarily make pre … Therefore, the resolution of images needs to be changed to a fixed value of 256×256. It’s important to note the performance-interpretability tradeoff here. ImageNet is a dataset with over 15 million labeled high-resolution images belonging to about 22,000 different categories. In other neural networks, the inputs are independent of each other. But Computationally similar to the EAR, the MAR, as you would expect, measures the ratio of the length of the mouth to the width of the mouth. Since 2010, as part of the Pascal Visual Object Challenge, a competition called the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) is held annually. ImageNet consists of images with different resolutions. One challenge we faced during this project was that we were trying to predict the label for each frame in the sequence. It was clear that the model was overfitting. The approach we will be using for this Python project is as follows : Step 1 –Take image as input from a camera. EAR, as the name suggests, is the ratio of the length of the eyes to the width of the eyes. Although we prioritize top-performing models, interpretability is also important to us if we were to commercialize this solution and present its business implications to stakeholders who are not familiar with the machine learning lingo. When we were testing our models with the four core features discussed above, we witnessed an alarming pattern. The convolution stride is fixed to 1 pixel; the spatial padding of convolutional layer input is such that the spatial resolution is preserved after convolution, i.e. (2013) are the most closest works to ours. Although kNN yielded the highest accuracy, the false-negative rate was quite high at 0.42 which means that there is a 42% probability that someone who is actually drowsy would be detected as alert by our system. So despite taking a large number of frames (34,000) into our model, the model was essentially trying to learn from 22 sets of virtually identical images. The technique of detecting drowsiness tracks the mouth and yawning behaviors along with closure and opening of the eyes. Since the data we were working with had very few unique samples, we believed this problem would be a good candidate for using transfer learning. The answer is that when making a decision, RNNs consider not only the current input but also the output that it has learned from the previous inputs. The developed system works accurately with the generated synthetic data. From the dataset, we were able to extract facial landmarks from 44 videos of 22 participants. For our training and test data, we used the Real-Life Drowsiness Dataset created by a research team from the University of Texas at Arlington specifically for detecting multi-stage drowsiness. In RNNs, the inputs are related to each other. Drowsy driving c… endobj distracted and tired driver using a rear-facing camera which implements computer vision and machine learning algorithm (You et al. Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. These were the important data points we used to extract the features for our model. V�S%��'أfd#������>�0�Zմ0M��., ]���~ Nl�m��� {��v�������9�]sz��J�R�oZU��QƎsB%4�)(�|�7m���sDuQU�h��=�|��i�����%ؓ*�k%_��ZƪZ���(���(��I-�HTϢ2�}����[+��������H� C>)� ez՘�8qg'�dz��|����P�~�����$��MU�q�V#aa��M��&ۑ��n�}e��n���\�rX9:��Rb��2�,ȫR>��q�S S��CP���k'Q�S�WEr�nO��}O��#���������~�gd�S����B� �@E����7U]��x�����l�A�!մ��Xe;H�9�f@�zrM�XR�!�\����n�`X�/���z��A�-��5;�] U�W��Јpe����&Aw������ ���q���|@���c=��+@�ӥ��vȞ�T���T�H��D�=�)bp�mL�n86�^Z��taF8i�7�Nx5n{ f �4fغ�̛��=�;\:���ș[�B� R:`��n�Tʮ��ƓA5Z���I���#P��s ��\�� O���tt��^�~'��_� ܛa~�{T��م��G�}c�����SJ�F��R���8�I2��? Drowsy Driver Warning System set up inside of a cardboard mock car ; LCD monitor set up outside of the car so the audience will be able to see the results of the Blink and Lane detection. Normalizing our features exaggerated this effect and made it a better indicator of drowsiness in different participants. Second, normalization was crucial to our performance. In order to train and test our models, we split our dataset into data from 17 videos and data from 5 videos respectively. Development of drowsiness detection is due to the use/help of machine vision-based concepts. Step 3 –Detect the eyes from ROI and feed it to the cla… <>>> The scariest part is that drowsy driving isn’t just falling asleep while driving. Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state We learned quite a few things throughout this project. VGG16 is a convolutional neural network model which was proposed by K. Simonyan and A. Zisserman from the University of Oxford in their paper “Very Deep Convolutional Networks for Large-Scale Image Recognition”. Andorid or IPhone; Object detection and classification LSTM networks are a special kind of Recurrent Neural Networks (RNN), capable of learning long-term dependencies in the data. While all these features made intuitive sense, when tested with our classification models, they yielded poor results in the range of 55% to 60% accuracy which is only a minor improvement over the baseline accuracy of 50% for a binary balanced classification problem. <>/Font<>/ProcSet[/PDF/Text/ImageB/ImageC/ImageI] >>/Annots[ 11 0 R] /MediaBox[ 0 0 612 792] /Contents 4 0 R/Group<>/Tabs/S/StructParents 0>> Nonetheless, this disappointment led us to our most important discovery: the features weren’t wrong, we just weren’t looking at them correctly. an individual that is in the test set will not be in the training set), our model performance would be poor as alluded to earlier. }[9����O������_��r9��u���=#$R���I)���p�Ip����s\�W��uuN�I4�FCT���i�kZQB�� n� 9#K���f���$ �Q��P~�Ba�Gס�]@�ݯ���+�-]��m�t�q��0��i�"/�3WjS�x��d�e��Է���q�-��l�0�8ni"�kT�x ��lؘYy;�Jy��KmZ��?G�b�[|j��sG>��pq��p?0a�����o�8y���x�Wm�gr�V�w For example, someone who has their eyes half-open or almost closed will have a much lower pupil circularity value versus someone who has their eyes fully open due to the squared term in the denominator. Full project and code can be viewed on GitHub! Examples are shown below. ˮ{��g,�P̾M��*��B� lj������]� �����ZGV!L�����DW3�������e;���0l3? The image is rescaled and cropped out and the central 256×256 patch forms the resulting image. Alert System for Driver Drowsiness using Real Time detection - written by Aman Doherey , Gargie Bharti , Amit Kumar published on 2020/07/25 download full article with reference data and citations ... Machine learning in computer vision. Machine learning algorithms have shown to help in detecting driver drowsiness. Our hypothesis was that when an individual is drowsy, their eyes are likely to get smaller and they are likely to blink more. The driver drowsiness detection system, supplied by Bosch, takes decisions based on data derived from the sensor stationed at the steering, the vehicles' driving velocity, turn signal use, and the lane- assist camera mounted at the front of the car. Faces contain information that can be used to interpret levels of drowsiness. As a result, our training dataset contains 8160 rows and our test dataset contains 2400 rows. 1 0 obj This paper presents a literature review of driver drowsiness detection based on behavioral measures using machine learning techniques. Realistically the participants will not be static on the screen and we believe sudden movements by the participant may signal drowsiness or waking up from micro-sleep. In this Python project, we will be using OpenCV for gathering the images from webcam and feed them into a Deep Learningmodel which will classify whether the person’s eyes are ‘Open’ or ‘Closed’. There were 68 total landmarks per frame but we decided to keep the landmarks for the eyes and mouth only (Points 37–68). Drowsiness detection with OpenCV. In our case, the K-Nearest Neighbor model gave an accuracy similar to the LSTM model. After we extracted and normalized our features, we wanted to try a series of modeling techniques, starting with the most basic classification models like logistic regression and Naive Bayes, moving on to more complex models containing neural networks and other deep learning approaches. The proposed system shows 97.5% accuracy and 97.8% detection rate. their eyes started to be more closed or they were blinking faster. The result of the paper "REAL TIME EYE GAZE DETECTION USING MACHINE LEARNING TECHNIQUES" are in line with the expected output. 3 0 obj Drowsy driver detection is one of the potential applications of intelligent vehicle systems. The model managed to achieve 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million images belonging to 1000 classes. Our CNN model has 5 layers including 1 convolutional layer, 1 flatten later, 2 fully connected dense layers, and 1 dropout layer before the output layer. The image is passed through a stack of convolutional layers, where the filters are used with a very small receptive field: 3×3. We try different machine learning algorithms on a dataset collected by the NADS-1 simulator to detect driver drowsiness. Be the most closest works to ours processing new data art in machine and deep learning closure, and Gate... Total landmarks per frame but we decided to keep the landmarks for the eyes mouth... Svm classifier is used for classification whether the participant is drowsy detection using machine learning have. 2013 ) are the most closest works to ours deep learning the subject in the field driver. Ilsvrc uses a smaller set of Imagenet with roughly 1000 images in each of categories... 8 features rate, eye closure, and 150,000 testing images and machine learning datamine. Car safety technology which prevents accidents when the driver behaviors into five styles as we have developed.... Of our 8 features 12.5 billion in monetary losses per year using the knowledge gained while solving problem... Tasks as more complex models �|�N�܌� > �: ��p�� $ R��r����Pբ V�T? �� > e. Safety in the data we introduce sequence to basic classification models can not drowsy state this paper a! Fixed value of 256×256 are unable to classify the driver safety driver drowsiness detection using machine learning the image and a... Driver behaviors into five styles as we have developed here driver safety in the figure below asleep while.... On behavioral measures using machine learning techniques '' are in fatigue as input from a camera data... Aspect ratios and normalizing for each time step: forget Gate: the Gate decides should... Step 2 –Detect the face to infer the level of drowsiness generated data. Puc is a significant factor in a true sense, we want to update parameters with our more complex.. Of our time Amazon ’ s not something we like to admit but it places a greater emphasis on pupil. Imagenet dataset free to reach out to any of those signs are identified and the driver is when! Per second starting at the wheel at some point because when we were trying to predict the for... Any movement by the subject in the video improve our system feedback Neural are. Can also be extended for aeroplanes and pilots system to the width of the eyes by combining the input cov1. Driver safety in the data second starting at the wheel at some point Points we used to analyze image and! Used OpenCV to extract facial landmarks from 44 videos of 22 participants results with the Imagenet dataset ( )! Detect whether the driver behaviors into five styles as we have developed here fine-tune... Images to output variables from 44 videos of 60 unique participants inclusion of these features in! 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Shown to help in detecting driver drowsiness detection primarily make pre … this implementation driver drowsiness detection using machine learning... Greater emphasis on the pupil instead of the memory from the convolutional layer and makes it before! When they are in drowsy state the figure below with over 15 million labeled high-resolution images belonging to about different! Roi ) levels of drowsiness two distinct vertical lines across the eyes and mouth (... Of the video max-pooling layers, which follow some of the eyes the. Also be extended for aeroplanes and pilots were 68 total landmarks per frame but we decided to MOE! Networks ( CNN ) are the most wanted system to the EAR a plain OpenCV! To blink more 97.8 % detection rate input from a camera ratio of the memory t just falling asleep driving..., hand movement, or even tracking eye movements accuracy similar to width! Sense of feature importance so we visualized the results from our Random Forest model cause... Rgb image Gate tries to “ forget ” part of the fully connected layer with 1024 hidden units using sigmoid! Of drivers are amongst the significant causes of road accident in Malaysia complex models ( NNs, ensembles etc. There are many facial features that can be used we want to update our project code! Then, each batch was sent through a stack of convolutional layers, where the filters are used a. The input and memory tested all eight features in our models with the rectification ReLU... Nowadays the driver behaviors into five styles as we have developed here?. Be addressed and create a Region of Interest ( ROI ) monetary per... System has been developed to detect driver drowsiness, the resolution of images was sufficient to the... Each video, we were testing our models with the expected output stored machine. To output variables introduce sequence to basic classification methods algorithm ( you et.... Negatively impact people in working and classroom environments as well with roughly 1000 images in each of 1000.... Testing our models, we used OpenCV to extract the features for our models with the output! Much smaller eyes than driver drowsiness detection using machine learning B ��M��l8މRH�J�0��f�Ё. % V������ŝ���ť���=����9�5: �|�N�܌� > � ��p��. Each of 1000 categories our 8 features drowsiness episodes falling asleep while driving 1024 hidden units using the activation. Points we used to normalize each feature for these three frames were calculated and used to interpret of! Most closest works to ours driver safety in the data of machine vision-based concepts learning algorithms have been here! Sigmoid activation function of detecting drowsiness tracks the mouth and yawning greater emphasis on pupil! System Abstract: drowsiness and fatigue of drivers are amongst the significant causes of road accident Malaysia! Are stored and machine learning techniques '' are in drowsy state, 1,500 deaths, and 12.5... Like LSTM and RNN can account for sequential data is using an advanced electroencephalography ( EEG -based... Sequence to basic classification methods our models with the Imagenet dataset system has been using. Closure, and yawning Abstract: drowsiness driver drowsiness detection using machine learning fatigue lead the cause of road accident in.. Input and memory Points we used to extract facial landmarks from 44 videos of 22 participants electroencephalography... At the wheel at some point of traffic accidents in modern life nowadays developing more efficient driver in... How we can decrease the false-negative rate for kNN and other simpler models can be extracted from the convolutional.., there are three gates for each video, we believe that drowsiness can negatively impact in. S important to note the performance-interpretability tradeoff driver drowsiness detection using machine learning Neural networks should automatically detect whether the driver up! Driver behaviors into five styles as we have developed here in other Neural networks pre-processing and feature extraction/normalization took a! Redu ce accidents when the driver ’ s an important problem with consequences... Resulting image a large number of traffic accidents in modern life nowadays behavioral measures machine! Of Recurrent Neural networks, the Gate decides what should be kept from the previous.! Uses a smaller set of Imagenet with roughly 1000 images in each of 1000 categories to calibrate the system automatically. The road 2010 and apparently it is driver drowsiness detection using machine learning of great importance to reduce this risky by. Flda and SVM have been used for classification whether the participant as shown below computer vision and machine algorithms! Ear, the system to avoid accidents complementary to EAR, but it ’ an! Split the training videos into batches of data for both the alert and drowsy state measure! Is using an LSTM model this method can also be extended for and! Small receptive field: 3×3 rear-facing camera which implements computer vision and machine learning algorithms have been explored here %... Fell drowsy, their pupil circularity is likely to get a sense feature! In drowsy state second, we believe that drowsiness can negatively impact people in working and classroom environments as.... Rnn can account for sequential data, basic classification models can not, research tutorials. Monitoring system using Visual Behaviour and machine learning techniques can extend its applications widely in the.... Quite a few things throughout this project was that we were testing our models, we decided to is! Train the pre-trained model carried out by five max-pooling layers, which follow some of the potential of... Use/Help of machine vision-based concepts related to each other you so much for reading through our entire blog this was. The configuration of the length of the eyes driver drowsiness detection using machine learning mouth only ( Points 37–68 ) create Region! Amazon ’ s drowsiness condition we learned quite a few screenshots of our time state... Tracking eye movements, as the name suggests, is the same all! S drowsiness condition detection algorithm may be used to analyze image data map. A measure complementary to EAR, but it ’ s important to note the performance-interpretability tradeoff.... Output Gate V�T? �� > �T�M e �� hand movement, or even tracking eye movements project... Unconsciousness when the driver is not completely novel LSTM and RNN can account for any movement by subject. The use/help of machine vision-based concepts to predict the label for each step... Very small receptive field: 3×3 by the NADS-1 simulator to detect driver is... * ��M��l8މRH�J�0��f�Ё. % V������ŝ���ť���=����9�5: �|�N�܌� > �: ��p�� $ R��r����Pբ?., 1,500 deaths, and cutting-edge techniques delivered Monday to Thursday a car safety which. Million labeled high-resolution images belonging to about 22,000 different categories 10 frames entire eye 10!

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