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Measuring Similarity in CCTV Systems for a Real-time Assessment of Traffic Jams

机译：测量CCTV系统中的相似度以实时评估交通拥堵

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摘要

Traffic jams are inevitable on the roads that many of us use every day. Their number and scale are generally increasing, especially in cities where economic activities are flourishing. The causes of these traffic jams are numerous and generally have economic and socio-environmental consequences. Many solutions have been proposed for detecting traffic jams without considering mathematical tools. In this article, we propose to provide solutions based on mathematical tools which make it possible to measure the similarity between two successive images acquired via closed circuit television (CCTV) systems. This similarity measure will allow us to assess the state of traffic jams in a CCTV system in order to prevent them. By analyzing the transmission of images through a variable sliding window, the implementation of the SSIM (Structural Similarity Index Measure) and the cross-correlation metrics which make possible to measure the similarity between two successive images in transmission in standardized Performance Evaluation of Tracking and Surveillance (PETS) datasets. The comparison between these two metrics based on the processing time and the probability distributions reveals that the SSIM metric provides better performance to prevent traffic jams.
机译：交通拥堵在许多我们每天使用的道路上都是不可避免的。他们的数量和规模一般都在增加，特别是在经济活动蓬勃发展的城市。这些交通拥堵的原因众多，通常具有经济和社会环境后果。已经提出了许多解决方案来检测交通拥堵而不考虑数学工具。在本文中，我们建议提供基于数学工具的解决方案，这使得可以测量通过闭路电视（CCTV）系统获取的两个连续图像之间的相似性。此相似度措施将允许我们评估CCTV系统中的交通拥堵状态，以防止它们。通过通过可变滑动窗口分析图像的传输，实现SSIM（结构相似性指数测量）和互相关度量的实现，这使得可以测量在跟踪和监视的标准化性能评估中传输中的两个连续图像之间的相似性（宠物）数据集。基于处理时间和概率分布的这两个度量之间的比较显示，SSIM度量提供更好的性能以防止交通拥堵。

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