论文标题

介绍基于树的技术,用于对象跟踪系统中的高效和实时标签检索

Introduction of a tree-based technique for efficient and real-time label retrieval in the object tracking system

论文作者

Benrazek, Ala-Eddine, Kouahla, Zineddine, Farou, Brahim, Seridi, Hamid, Allele, Imane

论文摘要

本文解决了大规模视频监视系统中移动对象的实时跟踪质量的问题。在跟踪过程中,系统将标识符或标签分配给每个跟踪对象,以将其与其他对象区分开。在这样的任务中,必须将标识符保留在相同对象的情况下,无论区域,外观的时间或检测摄像头都是至关重要的。这是为了节省有关跟踪对象的尽可能多的信息,减少ID开关数(ID-SW)并提高对象跟踪的质量。要完成对象标签,必须搜索相机收集的大量数据以检索最相似(最近的邻居)对象标识符。尽管此任务很简单,但在大规模的视频监视网络中它变得非常复杂,在该网络中,数据变得非常大。在这种情况下,随着这一增加的增加,标签检索时间大大增加,这对实时跟踪系统的性能产生了负面影响。为了避免此类问题,我们提出了一种新解决方案,以自动将多个对象标记,以使用索引机制有效地实时跟踪。该机制组织了自适应BCCF-Tree中检测和跟踪阶段中提取的物体的元数据。该结构的主要优点是:其索引的能力大规模元数据由多茶表产生,其对数搜索复杂性,这些搜索复杂性会隐含地降低搜索响应时间及其研究结果的质量,从而确保了跟踪对象的相干标记。系统负载是通过基于基础架构的新事物互联网进行分配的,以改善数据处理和实时对象跟踪性能。实验评估是在包含不同人群活动的多摄像机生成的公开数据集上进行的。

This paper addresses the issue of the real-time tracking quality of moving objects in large-scale video surveillance systems. During the tracking process, the system assigns an identifier or label to each tracked object to distinguish it from other objects. In such a mission, it is essential to keep this identifier for the same objects, whatever the area, the time of their appearance, or the detecting camera. This is to conserve as much information about the tracking object as possible, decrease the number of ID switching (ID-Sw), and increase the quality of object tracking. To accomplish object labeling, a massive amount of data collected by the cameras must be searched to retrieve the most similar (nearest neighbor) object identifier. Although this task is simple, it becomes very complex in large-scale video surveillance networks, where the data becomes very large. In this case, the label retrieval time increases significantly with this increase, which negatively affects the performance of the real-time tracking system. To avoid such problems, we propose a new solution to automatically label multiple objects for efficient real-time tracking using the indexing mechanism. This mechanism organizes the metadata of the objects extracted during the detection and tracking phase in an Adaptive BCCF-tree. The main advantage of this structure is: its ability to index massive metadata generated by multi-cameras, its logarithmic search complexity, which implicitly reduces the search response time, and its quality of research results, which ensure coherent labeling of the tracked objects. The system load is distributed through a new Internet of Video Things infrastructure-based architecture to improve data processing and real-time object tracking performance. The experimental evaluation was conducted on a publicly available dataset generated by multi-camera containing different crowd activities.

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