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Pedestrian Density Analysis in Public Scenes With Spatiotemporal Tensor Features

机译：具有时空张量特征的公共场景行人密度分析

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摘要

Pedestrian density estimation is one of the key problems in intelligent transportation systems and has been widely applied to a number of applications in other fields of engineering. Counting-by-regression methods are more favorable for coping with such a problem owing to their robustness against interperson occlusion and relaxing the impractical requirement of a high video frame rate, compared to counting-by-detection and counting-by-clustering methods. However, imagery features in the existing counting-by-regression approaches are extracted from the whole region or spatially localized cells/pixels of each single video frame, which omits the unique motion patterns of the same pedestrians across the neighboring frames. In the light of this, this paper exploits a novel tensor-formed spatiotemporal feature representation and applies it in a multilinear regression learning framework, which can capture spatially distributed dynamic crowd patterns by discovering the latent multidimensional structural correlations of tensor features along both spatial (i.e., horizontal and vertical) and temporal dimensions. Extensive evaluation with the public UCSD and Shopping Mall benchmarks demonstrate superior performance of our approach to the state-of-the-art counting methods even when the surveillance data has a low frame rate.
机译：行人密度估计是智能交通系统中的关键问题之一，已广泛应用于其他工程领域的许多应用中。与基于检测的计数和基于聚类的方法相比，基于回归的计数方法由于其对人际遮挡的鲁棒性和放宽了高视频帧速率的不切实际的要求，因此更有利于解决此类问题。然而，现有的按回归计数的方法中的图像特征是从每个单个视频帧的整个区域或空间局部的单元/像素中提取的，这省略了相同行人跨越相邻帧的独特运动模式。有鉴于此，本文利用了一种新颖的张量形成的时空特征表示并将其应用于多线性回归学习框架，该框架可以通过发现沿两个空间（即，即两个方向）的张量特征的潜在多维结构相关性来捕获空间分布的动态人群模式。 ，水平和垂直）和时间维度。使用公开的UCSD和Shopping Center基准进行的广泛评估表明，即使监视数据的帧速率较低，我们的方法仍可实现最新计数方法的卓越性能。

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