鲁棒、可扩展、快速：用于多摄像机网络中车辆跟踪和车辆重新识别的组合分类器
Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking and Vehicle Re-ID in Multi-Camera Networks
摘要：
随着摄像机网络在过去十年中变得越来越普遍，视频管理的研究兴趣已经转移到多摄像机网络上的分析。这包括执行任务，如目标检测，属性识别，以及车辆/人员跟踪跨不同的相机没有重叠。当前的管理框架是为封闭数据集环境中的多摄像机网络设计的，在封闭数据集环境中，摄像机的可变性有限，并且监控环境的特性是众所周知的。此外，目前的框架是为离线分析而设计的，并有人类操作员的指导，用于法医应用。本文提出了一种用于异构多摄像机网络中视频分析的组合分类器框架，该网络具有对抗性条件，例如多尺度，多分辨率摄像机捕获具有不同遮挡，模糊和方向的环境。我们描述了车辆跟踪和车辆重新识别（重新识别）的实现，其中我们实现了一个零样本学习（zero-shot learning）系统，该系统始终对所有车辆进行自动跟踪。我们对VeRi-776和Cars 196的评估表明，与当前离线视频分析方法相比，团队分类器框架对对抗性条件具有鲁棒性，可扩展到不断变化的视频特征，如新的车辆类型/品牌和新的摄像头，并提供实时性能。
英文摘要：
As camera networks have become more ubiquitous over the past decade, the research interest in video management has shifted to analytics on multi-camera networks. This includes performing tasks such as object detection, attribute identification, and vehicle/person tracking across different cameras without overlap. Current frameworks for management are designed for multi-camera networks in a closed dataset environment where there is limited variability in cameras and characteristics of the surveillance environment are well known. Furthermore, current frameworks are designed for offline analytics with guidance from human operators for forensic applications. This paper presents a teamed classifier framework for video analytics in heterogeneous many-camera networks with adversarial conditions such as multi-scale, multi-resolution cameras capturing the environment with varying occlusion, blur, and orientations. We describe an implementation for vehicle tracking and vehicle re-identification (re-id), where we implement a zero-shot learning (ZSL) system that performs automated tracking of all vehicles all the time. Our evaluations on VeRi-776 and Cars196 show the teamed classifier framework is robust to adversarial conditions, extensible to changing video characteristics such as new vehicle types/brands and new cameras, and offers real-time performance compared to current offline video analytics approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
