Abstract: With the intervention of social media and internet technology, people are getting more and more careless and distracted while driving which is having a severe detrimental effect on the safety of the driver and his fellow passengers. To provide an effective solution, this paper put forwards a Machine Learning model using Convolutional Neural Networks to not only detect the distracted driver but also identify the cause of his distraction by analyzing the images obtained using the camera module installed inside the vehicle. Convolutional neural networks are known to learn spatial features from images, which can be further examined by fully connected neural networks. The experimental results show a 99% average accuracy in distraction recognition and hence strongly support that our Convolutional neural networks model can be used to identify distraction among the drivers.
Conclusion: Deep learning using Convolutional Neural Networks  has become a hot area in Machine Learning research, and it has been extensively used in image classification, voice recognition, etc. In this paper, we use Deep Convolutional Networks for detecting distracted drivers and also identifying the cause of their distraction using the VGG16  and VGG19  model. The above results suggest that the methods discussed in this work can be used to develop a system using which distraction while driving can be detected among drivers. The model proposed can automatically identify any of the mentioned 10 classes of distraction and identify not only basic distraction but also their cause of distraction. With an accuracy of more than 99%, the mentioned system was shown to be efficient and workable. The proposed system can be a part of some Driver State Monitoring System which will effectively monitor the state of the driver while he is driving. Driver state monitoring has been becoming increasingly popular these days and many automobile giants have started adopting such systems as a methodology to prevent accidents. These systems, when installed inside vehicles will raise warnings whenever the driver gets distracted, thus trying to prevent any accidents due to distraction from the driver. Also in this work a significant amount of training time has been shown to be reduced. When pre-trained weights from ImageNet  were not used, the training time increased by around 50 times for both VGG16  and VGG19 . A graphical representation of time elapsed is depicted in Fig. 15. This drastic reduction in training time was achieved without diminishing the accuracy of our classification models. In future work as an extension to this work, more categories of distraction can be brought in. Even considering certain specific scenarios, which were not targeted in the present work, such as detecting drowsiness among drivers may also provide an opportunity to widen the scale of the work and build a more efficient system.
ABSTRACT In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small ( 3 × 3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision
CONCLUSION In this work we evaluated very deep convolutional networks (up to 19 weight layers) for largescale image classification. It was demonstrated that the representation depth is beneficial for the classification accuracy, and that state-of-the-art performance on the ImageNet challenge dataset can be achieved using a conventional ConvNet architecture (LeCun et al., 1989; Krizhevsky et al., 2012) with substantially increased depth. In the appendix, we also show that our models generalise well to a wide range of tasks and datasets, matching or outperforming more complex recognition pipelines built around less deep image representations. Our results yet again confirm the importance of depth in visual representations.
Pra quem acompanha o Python Programming, sabe que sempre quando eles postam algo é que coisa boa vem aí; e dessa vez não foi diferente.
O Harrison está fazendo uma série de posts sobre como jogar GTA V usando Deep Learning com Tensor Flow usando CNN (convolutional neural network).
Este é o primeiro vídeo da série em que ele faz o setup da solução:
E essa é a última versão treinada:
Para quem estiver interessado o Harrison deixou uma playlist com todos os estágios do treinamento, e um BOT rodando sozinho em um livestream (vale a pena ver o quão divertido é ver o bot tentando dirigir).
Mais um caso de aplicação de Deep Learning em questões médicas.
Abstract: Parkinson’s Disease (PD) is a chronic and progressive illness that affects hundreds of thousands of people worldwide. Although it is quite easy to identify someone affected by PD when the illness shows itself (e.g. tremors, slowness of movement and freezing-of-gait), most works have focused on studying the working mechanism of the disease in its very early stages. In such cases, drugs can be administered in order to increase the quality of life of the patients. Since the beginning, it is well-known that PD patients feature the micrography, which is related to muscle rigidity and tremors. As such, most exams to detect Parkinson’s Disease make use of handwritten assessment tools, where the individual is asked to perform some predefined tasks, such as drawing spirals and meanders on a template paper. Later, an expert analyses the drawings in order to classify the progressive of the disease. In this work, we are interested into aiding physicians in such task by means of machine learning techniques, which can learn proper information from digitized versions of the exams, and them recommending a probability of a given individual being affected by PD depending on its handwritten skills. Particularly, we are interested in deep learning techniques (i.e. Convolutional Neural Networks) due to their ability into learning features without human interaction. Additionally, we propose to fine-tune hyper-arameters of such techniques by means of meta-heuristic-based techniques, such as Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization.