5,033 research outputs found

    Project of implementing an intelligent system into a Raspberry Pi based on deep learning for face detection and recognition in real-time

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    Artificial Intelligence (AI) is among most important fields of knowledge and applications in a large variety of domains. Recently however, it has become a trending research topic propelled by Cloud computing, social networks and alike. Terms like machine learning, "Big Data" and artificial neural networks very frequently appear not only in scientific media but even in the mass media. In this project, we aim to design, implement and evaluate an AI technique, namely, deep learning, which has become very popular for face recognition. The problem is formulated from an engineering perspective: to design a small size system based on Raspberry Pi and an attached camera to it to detect and recognise human faces in real time. It should be mentioned that while for humans face recognition is a trivial task, we do it every day and with a full accuracy, for a computer, this is complex task. Recent applications from many industries show a large potential of intelligent systems that need to recognise faces with high accuracy. The thesis is essentially structured into two main parts. In the first part we formulate the problem, analyse potential solutions and propose a solution for its resolution. In the second part of the project we develop the proposed solution into an implementation of an intelligent system for a computationally limited and physical portable device (Raspberry Pi). The solution is empirically evaluated in terms of accuracy and performance using real data sets. The relevance of using such a small size intelligent system relies in the fact that this application can be installed in other devices, such as drones, easily, at low cost and without compromising the performance and speed of the said intelligent system

    Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras

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    Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full image results in multiple image blocks or windows to analyze, which could make the time performance of the system very poor when implemented on low cost devices. This paper presents a system which attempts to detect abnormal moving objects within an area covered by a PTZ camera while it is panning. The decision about the block of the image to analyze is based on a mixture distribution composed of two components: a uniform probability distribution, which represents a blind random selection, and a mixture of Gaussian probability distributions. Gaussian distributions represent windows in the image where anomalous objects were detected previously and contribute to generate the next window to analyze close to those windows of interest. The system is implemented on a Raspberry Pi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
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