A fast approach to novelty detection in video streams using recursive density estimation

R. Ramezani, P. Angelov, Xiaowei Zhou
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引用次数: 38

Abstract

Video-based surveillance and security become extremely important in the new, 21st century for human safety, counter-terrorism, traffic control etc. Visual novelty detection and tracking are key elements of such activities. The current state-of-the-art approaches often suffer from high computational, memory storage costs and from not being fully automated (they usually require a human operator in the loop). This paper introduces a new approach to the problem of novelty detection in video streams that is based on recursive, and therefore, computationally efficient density estimation by a Cauchy type of kernel (as opposed to the usually used Gaussian one). The idea of the proposed approach stems from the recently introduced evolving clustering approach, eClustering and is suitable for online and real-time applications in fully autonomous and unsupervised systems as a stand-alone novelty detector or for priming a tracking algorithm. The approach proposed in this paper has evolving property - it can gradually update the background model and the criteria to detect novelty by unsupervised online learning. The proposed approach is faster by an order of magnitude than the well known kernel density estimation (KDE) method for background subtraction, while having has adaptive characteristics, and does not need any threshold to be pre-specified. Recursive expressions similar to the proposed approach in this paper can also be applied to image segmentation and landmark recognition used for self-localization in robotics. If combined with a real-time prediction using Kalman filter or evolving Takagi-Sugeno fuzzy models a fast and fully autonomous tracking system can be realized with potential applications in surveillance and robotic systems.
基于递归密度估计的视频流新颖性快速检测方法
在新的21世纪,基于视频的监控和安防在人类安全、反恐、交通管制等方面变得极其重要。视觉新颖性检测和跟踪是此类活动的关键要素。目前最先进的方法通常存在计算和内存存储成本高以及不完全自动化的问题(它们通常需要人工操作)。本文介绍了一种基于递归的视频流新颖性检测问题的新方法,因此,通过柯西核类型(与通常使用的高斯核类型相反)进行计算效率高的密度估计。所提出的方法的思想源于最近引入的不断发展的聚类方法,ecclustering,适用于完全自主和无监督系统中的在线和实时应用,作为独立的新颖性检测器或启动跟踪算法。本文提出的方法具有进化特性,它可以通过无监督在线学习逐步更新背景模型和新颖性检测标准。该方法比已知的核密度估计(KDE)方法的背景减除速度快一个数量级,同时具有自适应特性,并且不需要预先指定任何阈值。与本文提出的方法类似的递归表达式也可以应用于机器人中用于自定位的图像分割和地标识别。如果结合使用卡尔曼滤波或进化Takagi-Sugeno模糊模型的实时预测,可以实现快速和完全自主的跟踪系统,在监视和机器人系统中具有潜在的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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