Deep Learning and Approach for Tracking People’s Movements in a Video

Jemai Bornia, A. Frihida, Olivier Debauche, S. Mahmoudi, P. Manneback
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Abstract

Everyday, a large amount of data is produced thanks to technological advances in the field of multimedia, associated with the generalization of their use in many applications. The need to keep control over this content, in terms of data analysis, classification, accurate AI (Artificial Intelligence) algorithms are required to perform this task efficiently and quickly. In this article, we propose an approach using deep learning technologies for the analysis of movement in video sequences. The suggested approach uses images from video splitting to detect objects / entities present and store their descriptions in a standard XML file. As result, we provide a Deep Learning algorithm using TensorFlow for tracking motion and animated entities in video sequences.
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视频中人物运动的深度学习与跟踪方法
由于多媒体领域的技术进步,每天都会产生大量的数据,这与多媒体在许多应用中的广泛使用有关。需要保持对这些内容的控制,在数据分析、分类方面,需要准确的AI(人工智能)算法来高效、快速地执行这项任务。在本文中,我们提出一种方法使用深度学习技术分析视频序列的运动。建议的方法使用来自视频分割的图像来检测存在的对象/实体,并将其描述存储在标准XML文件中。因此,我们提供了一种使用TensorFlow的深度学习算法来跟踪视频序列中的运动和动画实体。
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