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Multiple human tracking system for unpredictable trajectories

机译：多人跟踪系统，可预测不可预测的轨迹

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摘要

Tracking multiple objects into a scene is one of the most active research topics in computer vision. The art of identifying each target within the scene along a video sequence has multiple issues to be solved, being collision and occlusion events among the most challenging ones. Because of this, when dealing with human detection, it is often very difficult to obtain a full body image, which introduces complexity in the process. The task becomes even more difficult when dealing with unpredictable trajectories, like in sport environments. Thus, head-shoulder omega shape becomes a powerful tool to perform the human detection. Most of the contributions to this field involve a detection technique followed by a tracking system based on the omega-shape features. Based on these works, we present a novel methodology for providing a full tracking system. Different techniques are combined to both detect, track and recover target identifications under unpredictable trajectories, such as sport events. Experimental results into challenging sport scenes show the performance and accuracy of this technique. Also, the system speed opens the door for obtaining a real-time system using GPU programing in standard desktop machines, being able to be used in higher-level human behavioral systems, with multiple applications.
机译：将多个对象跟踪到场景中是计算机视觉中最活跃的研究主题之一。沿着视频序列识别场景中每个目标的技术有多个问题需要解决，其中最具有挑战性的是碰撞和遮挡事件。因此，在进行人体检测时，通常很难获得全身图像，这会增加过程的复杂性。在诸如运动环境中处理不可预测的轨迹时，任务变得更加困难。因此，头肩欧米茄形状成为执行人体检测的有力工具。对这一领域的大多数贡献涉及检测技术，随后是基于欧米茄形状特征的跟踪系统。基于这些工作，我们提出了一种用于提供完整跟踪系统的新颖方法。结合了不同的技术来检测，跟踪和恢复在不可预测的轨迹（例如体育赛事）下的目标标识。在具有挑战性的运动场景中的实验结果表明了该技术的性能和准确性。而且，系统速度为在标准台式机中使用GPU编程获取实时系统打开了大门，该系统可以在具有多种应用程序的更高级的人类行为系统中使用。

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