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An adaptive people counting system with dynamic features selection and occlusion handling

机译：具有动态特征选择和遮挡处理的自适应人数统计系统

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摘要

This paper presents an adaptive crowd counting system for video surveillance applications. The proposed method is composed of a pair of collaborative Gaussian process models (GP) with different kernels, which are designed to count people by taking the level of occlusion into account. The level of occlusion is measured and compared with a predefined threshold for regression model selection for each frame. In addition, the proposed method dynamically identifies the best combination of features for people counting. The Mall and UCSD datasets are used to evaluate the proposed method. The results show that the proposed method offers a higher accuracy when compared against state of the art methods reported in open literature. The mean absolute error (MAE), mean squared error (MSE) and the mean deviation error (MDE) for the proposed algorithm are 2.90, 13.70 and 0.095, respectively, for the Mall dataset and 1.63, 4.32 and 0.066, respectively, for UCSD dataset. (C) 2016 Elsevier Inc. All rights reserved.
机译：本文提出了一种适用于视频监控的自适应人群计数系统。所提出的方法由一对具有不同内核的协作高斯过程模型（GP）组成，这些模型旨在通过考虑遮挡程度来对人员进行计数。测量遮挡的水平，并将其与预定义的阈值进行比较，以选择每个帧的回归模型。另外，所提出的方法动态地识别用于人数统计的特征的最佳组合。 Mall和UCSD数据集用于评估所提出的方法。结果表明，与公开文献中报道的最新方法相比，该方法具有更高的准确性。对于Mall数据集，所建议算法的平均绝对误差（MAE），均方误差（MSE）和平均偏差误差（MDE）对于Mall数据集分别为2.90、13.70和0.095，对于UCSD分别为1.63、4.32和0.066数据集。 （C）2016 Elsevier Inc.保留所有权利。

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