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-Smart Objects and the Internet of Things are two ideas which describe the future, walk together, and complement each other. Thus, the interconnection among objects can make them more intelligent or expand their intelligence to unsuspected limits. This could be achieved with a new network that interconnects each object around the world. However, to achieve this goal, the objects need a network that supports heterogeneous and ubiquitous objects, a network where exists more traffic among objects than among humans, but supporting for both types. For these reasons, both concepts are very close. Cities, houses, cars, machines, or any another object that can sense, respond, work, or make easier the lives of their owner. This is a part of the future, an immediate future. Notwithstanding, first of all, there are to resolve a series of problems. The most important problem is the heterogeneity of objects. This article is going to show a theoretical frame and the related work about Smart Object. The article will explain what are Smart Objects, doing emphasis in their difference with NotSmart Objects. After, we will present one of the different object classification system, in our opinion, the most complete.

Abstract-Could we use Computer Vision in the Internet of Things for using pictures as sensors? This is the principal hypothesis that we want to resolve. Currently, in order to create safety areas, cities, or homes, people use IP cameras. Nevertheless, this system needs people who watch the camera images, watch the recording after something occurred, or watch when the camera notifies them of any movement. These are the disadvantages. Furthermore, there are many Smart Cities and Smart Homes around the world. This is why we thought of using the idea of the Internet of Things to add a way of automating the use of IP cameras. In our case, we propose the analysis of pictures through Computer Vision to detect people in the analysed pictures. With this analysis, we are able to obtain if these pictures contain people and handle the pictures as if they were sensors with two possible states. Notwithstanding, Computer Vision is a very complicated field. This is why we needed a second hypothesis: Could we work with Computer Vision in the Internet of Things with a good accuracy to automate or semi-automate this kind of events? The demonstration of these hypotheses required a testing over our Computer Vision module to check the possibilities that we have to use this module in a possible real environment with a good accuracy. Our proposal, as a possible solution, is the analysis of entire sequence instead of isolated pictures for using pictures as sensors in the Internet of Things.

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To carry out this idea, he proposed to make a child's brain instead of trying to make an adult brain, and thus educate it to obtain the brain of an adult. In this way, he divides the problem into two parts: the 'child' program and the education process. He explains that we should not expect the first 'child' machine to come out on the first attempt and that we should teach it to see how its learning evolves. Thus, after several attempts, they would be getting better machines (or even worse), something that Turing compared with the process of evolution and that several researchers would later develop under the name of genetic algorithms [3].

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