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Automatic security system for recognizing unexpected motions through video surveillance

机译：自动安全系统，可通过视频监控识别意外动作

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This paper deals with a study concerning the so called "smart video surveillance" system, starting from the consideration of unexpected motions. It is known that security staff, whose aim is to watch monitors and approach in case something bad and unlawful happens, in any kind of location, can keep the attention up for no more than twenty minutes, then the concentration falls down severely. Since this decrease of efficacy, it may be helpful a support system in watching and analysing the real-time or recorded scenes. According to the state of the art, unlike several other examples of smart video surveillance techniques (3,4,5,6), this one described in this article does not focus on the images, it does rather on the velocity parameter. Given a certain scenario, certain behaviours, thereby velocities, are expected, and it is supposed they might happen. The anomalies recognition is done using artificial neural networks, which are built and trained in order to compare an array (target) of expected velocities in normal conditions of life, to several arrays (input) of other velocities extracted from unexpected situations properly chosen. Once decided the tool for tracking the motions in the videos, then obtained the arrays of x and y velocities components, it is the time to build the artificial neural networks through iterations, which have been done changing the number of hidden neurons. The results are interesting enough and go right to the purpose, which consists in the choice of the best neural network.
机译：本文从对意外运动的考虑入手，对所谓的“智能视频监控”系统进行了研究。众所周知，旨在在任何地方发生不良事件和非法事件的情况下，监视并采取监视措施的安全人员可以将注意力保持在不超过二十分钟的时间内，然后注意力会严重下降。由于功效的降低，因此在观看和分析实时或记录场景时，支持系统可能会有所帮助。根据现有技术，与智能视频监视技术的其他几个示例（3、4、5、6）不同，本文中介绍的这一方法不关注图像，而是关注速度参数。在给定的情况下，可以预期某些行为，从而可以预测速度，并且认为它们可能会发生。异常识别是使用人工神经网络，其中构建和训练，以比较在正常的生活条件的预期速度的阵列（目标），以从适当选择的意外情况下提取的其他速度的几个阵列（输入）来完成。一旦确定了用于跟踪视频中运动的工具，然后获得了x和y速度分量的数组，就该通过迭代构建人工神经网络了，该迭代已完成，可以更改隐藏的神经元的数量。结果足够有趣并且可以达到目的，这包括选择最佳的神经网络。

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