Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949

LEARNING SALIENCY FOR HUMAN ACTION RECOGNITION

PhDWhen we are looking at a visual stimuli, there are certain areas that stand out from the neighbouring areas and immediately grab our attention. A map that identi- es such areas is called a visual saliency map. As humans can easily recognize actions when watching videos, having their saliency maps available might be bene cial for a fully automated action recognition system. In this thesis we look into ways of learning to predict the visual saliency and how to use the learned saliency for action recognition. In the rst phase, as opposed to the approaches that use manually designed fea- tures for saliency prediction, we propose few multilayer architectures for learning saliency features. First, we learn rst layer features in a two layer architecture using an unsupervised learning algorithm. Second, we learn second layer features in a two layer architecture using a supervision from recorded human gaze xations. Third, we use a deep architecture that learns features at all layers using only supervision from recorded human gaze xations. We show that the saliency prediction results we obtain are better than those obtained by approaches that use manually designed features. We also show that using a supervision on higher levels yields better saliency prediction results, i.e. the second approach outperforms the rst, and the third outperforms the second. In the second phase we focus on how saliency can be used to localize areas that will be used for action classi cation. In contrast to the manually designed action features, such as HOG/HOF, we learn the features using a fully supervised deep learning architecture. We show that our features in combination with the predicted saliency (from the rst phase) outperform manually designed features. We further develop an SVM framework that uses the predicted saliency and learned action features to both localize (in terms of bounding boxes) and classify the actions. We use saliency prediction as an additional cost in the SVM training and testing procedure when inferring the bounding box locations. We show that the approach in which saliency cost is added yields better action recognition results than the approach in which the cost is not added. The improvement is larger when the cost is added both in training and testing, rather than just in testing

CentralNet: a Multilayer Approach for Multimodal Fusion

This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media. While most of the past multimodal approaches either work by projecting the features of different modalities into the same space, or by coordinating the representations of each modality through the use of constraints, our approach borrows from both visions. More specifically, assuming each modality can be processed by a separated deep convolutional network, allowing to take decisions independently from each modality, we introduce a central network linking the modality specific networks. This central network not only provides a common feature embedding but also regularizes the modality specific networks through the use of multi-task learning. The proposed approach is validated on 4 different computer vision tasks on which it consistently improves the accuracy of existing multimodal fusion approaches

Review of Face Detection Systems Based Artificial Neural Networks Algorithms

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa

Evaluation of CNN architectures for gait recognition based on optical flow maps

This work targets people identification in video based on the way they walk (\ie gait) by using deep learning architectures. We explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (\ie optical flow components). The low number of training samples for each subject and the use of a test set containing subjects different from the training ones makes the search of a good CNN architecture a challenging task.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec