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Proceedings ArticleDOI

Eye state detection for use in advanced driver assistance systems

TL;DR: A robust system to continuously track the driver's eye and detect its state (open/close) is proposed and the rate at which predictions are made is higher than existing systems.
Abstract: Most automobiles lack reliable smart systems that can constantly track the driver's behaviour and raise alarms as required. Extant systems are either too slow or not robust enough to cope with different types of drivers and conditions. In this paper, a robust system to continuously track the driver's eye and detect its state (open/close) is proposed. Frames from a live camera feed are constantly processed. Viola Jones algorithm, using Haar filters extracts the eye. The extraction is efficient with and without spectacles (translucent) and the system can even estimate the Region of Interest (RoI) where it is most likely to find the eye in the event that no eyes are explicitly detected. A trained CNN model using the LeNet architecture classifies the extracted eyes. The rate at which predictions are made is also higher than existing systems. The system raises an alarm if, after analysing the data points, it detects any anomalies.
Citations
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Posted Content
15 Jan 2020
TL;DR: Estimation of driver head direction for distraction detection, introduction of a new comprehensive dataset to detect eye closure, and presentation of three networks in which one is a fully designed deep neural network (FD-DNN) and others use transfer learning with VGG16 and VGG19 with extra designed layers (TL-VGG).
Abstract: This paper presents a novel approach and a new dataset for the problem of driver drowsiness and distraction detection. Lack of an available and accurate eye dataset strongly feels in the area of eye closure detection. Therefore, a new comprehensive dataset is proposed, and a study on driver distraction of the road is provided to supply safety for the drivers. A deep network is also designed in such a way that two goals of real-time application, including high accuracy and fastness, are considered simultaneously. The main purposes of this article are as follows: Estimation of driver head direction for distraction detection, introduce a new comprehensive dataset to detect eye closure, and also, presentation of three networks in which one of them is a fully designed deep neural network (FD-DNN) and others use transfer learning with VGG16 and VGG19 with extra designed layers (TL-VGG). The experimental results show the high accuracy and low computational complexity of the estimations and the ability of the proposed networks on drowsiness detection.

4 citations

Journal ArticleDOI
21 Dec 2020-Symmetry
TL;DR: In this article, the authors proposed an illumination condition optimized network (ICONet) to solve the symmetry of the needs of edge-oriented detection under complex illumination condition environments and the scarcity of related approaches.
Abstract: With the increasing popularity of artificial intelligence, deep learning has been applied to various fields, especially in computer vision. Since artificial intelligence is migrating from cloud to edge, deep learning nowadays should be edge-oriented and adaptive to complex environments. Aiming at these goals, this paper proposes an ICONet (illumination condition optimized network). Based on OTSU segmentation algorithm and fuzzy c-means clustering algorithm, the illumination condition classification subnet increases the environmental adaptivity of our network. The reduced time complexity and optimized size of our convolutional neural network (CNN) model enables the implementation of ICONet on edge devices. In the field of fatigue driving, we test the performance of ICONet on YawDD and self-collected datasets. Our network achieves a general accuracy of 98.56% and our models are about 590 kilobytes. Compared to other proposed networks, the ICONet shows significant success and superiority. Applying ICONet to fatigue driving detection is helpful to solve the symmetry of the needs of edge-oriented detection under complex illumination condition environments and the scarcity of related approaches.

2 citations

References
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Proceedings Article
01 Jan 2015
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Abstract: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

111,197 citations


"Eye state detection for use in adva..." refers methods in this paper

  • ...The Adam optimizer [11] is used instead of basic gradient descent to reduce the time taken to train the model....

    [...]

Journal ArticleDOI
01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

42,067 citations

Proceedings ArticleDOI
01 Dec 2001
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Abstract: This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "integral image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

18,620 citations


"Eye state detection for use in adva..." refers methods in this paper

  • ...The algorithm uses Haar basic feature [9] filters to detect the important features in the face like the eyes....

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  • ...The eyes in this region are found using Haar cascades [9]....

    [...]

  • ...The proposed system gives the driver instantaneous feedback based on LeNet [8], a convolution neural network, and the Viola Jones algorithm [9]....

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  • ...Facial Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper [9]....

    [...]

Proceedings Article
21 Jun 2010
TL;DR: Restricted Boltzmann machines were developed using binary stochastic hidden units that learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset.
Abstract: Restricted Boltzmann machines were developed using binary stochastic hidden units. These can be generalized by replacing each binary unit by an infinite number of copies that all have the same weights but have progressively more negative biases. The learning and inference rules for these "Stepped Sigmoid Units" are unchanged. They can be approximated efficiently by noisy, rectified linear units. Compared with binary units, these units learn features that are better for object recognition on the NORB dataset and face verification on the Labeled Faces in the Wild dataset. Unlike binary units, rectified linear units preserve information about relative intensities as information travels through multiple layers of feature detectors.

14,799 citations


"Eye state detection for use in adva..." refers methods in this paper

  • ...The activation function used in the model is the ReLU function [10] rather than the conventional sigmoid function since ReLU performs better....

    [...]

Book ChapterDOI
01 Jan 2001
TL;DR: Various methods applied to handwritten character recognition are reviewed and compared and Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques.
Abstract: Multilayer Neural Networks trained with the backpropagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture, Gradient-Based Learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation, recognition, and language modeling. A new learning paradigm, called Graph Transformer Networks (GTN), allows such multi-module systems to be trained globally using Gradient-Based methods so as to minimize an overall performance measure. Two systems for on-line handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of Graph Transformer Networks. A Graph Transformer Network for reading bank check is also described. It uses Convolutional Neural Network character recognizers combined with global training techniques to provides record accuracy on business and personal checks. It is deployed commercially and reads several million checks per day.

3,417 citations


"Eye state detection for use in adva..." refers methods in this paper

  • ...The proposed system gives the driver instantaneous feedback based on LeNet [8], a convolution neural network, and the Viola Jones algorithm [9]....

    [...]

  • ...The back-propagation algorithm is used in LeNet [8], to calculate the gradient....

    [...]

  • ...RELATED WORKS A. LeNet Architecture LeNet is a feed-forward Convolutional Neural Network which was built to classify digits in the MNIST dataset....

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  • ...Keywords: Eye Detection, Convolutional Neural Network, Deep Learning, LeNet, Driver Safety....

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  • ...The network uses a modified LeNet architecture [8]....

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