Measuring and analyzing the flow of customers in retail stores is essential for a retailer to better comprehend customers’ behavior and support decision-making. Nevertheless, not much attention has been given to the development of novel technologies for automatic people counting. We introduce LRCN-RetailNet: a recurrent neural network architecture capable of learning a non-linear regression model and accurately predicting the people count from videos captured by low-cost surveillance cameras. The input video format follows the recently proposed RGBP image format, which is comprised of color and people (foreground) information. Our architecture is capable of considering two relevant aspects: spatial features extracted through convolutional layers from the RGBP images; and the temporal coherence of the problem, which is exploited by recurrent layers. We show that, through a supervised learning approach, the trained models are capable of predicting the people count with high accuracy. Additionally, we present and demonstrate that a straightforward modification of the methodology is effective to exclude salespeople from the people count. Comprehensive experiments were conducted to validate, evaluate and compare the proposed architecture. Results corroborated that LRCN-RetailNet remarkably outperforms both the previous RetailNet architecture, which was limited to evaluating a single image per iteration; and two state-of-the-art neural networks for object detection. Finally, computational performance experiments confirmed that the entire methodology is effective to estimate people count in real-time.

衡量和分析零售商店中的客户流对于零售商更好地理解客户的行为并支持决策至关重要。然而，对于自动人数计数的新技术的开发并未给予太多关注。我们介绍了LRCN-RetailNet：这是一种递归神经网络体系结构，能够学习非线性回归模型并从低成本监控摄像机捕获的视频中准确预测人数。输入视频格式遵循最近提出的RGBP图像格式，该格式由颜色和人物（前景）信息组成。我们的架构能够考虑两个相关方面：通过卷积层从RGBP图像中提取的空间特征；以及问题的时间连贯性，这是由循环层利用的。我们表明，通过有监督的学习方法，训练有素的模型能够准确预测人数。此外，我们介绍并证明了对方法的直接修改可以有效地将销售人员从人数中排除。进行了全面的实验，以验证，评估和比较所提出的体系结构。结果证实了LRCN-RetailNet明显优于以前的RetailNet体系结构，后者仅限于每次迭代评估一个图像。和两个用于对象检测的最新神经网络。最后，计算性能实验证实，整个方法可以有效地实时估计人数。经过训练的模型能够准确预测人数。此外，我们介绍并证明了对方法的直接修改可以有效地将销售人员从人数中排除。进行了全面的实验，以验证，评估和比较所提出的体系结构。结果证实了LRCN-RetailNet明显优于以前的RetailNet体系结构，后者仅限于每次迭代评估一个图像。和两个用于对象检测的最新神经网络。最后，计算性能实验证实，整个方法可以有效地实时估计人数。经过训练的模型能够准确预测人数。此外，我们介绍并证明了对方法的直接修改可以有效地将销售人员从人数中排除。进行了全面的实验，以验证，评估和比较所提出的体系结构。结果证实了LRCN-RetailNet明显优于以前的RetailNet体系结构，后者仅限于每次迭代评估一个图像。和两个用于对象检测的最新神经网络。最后，计算性能实验证实，整个方法可以有效地实时估计人数。我们提出并证明，对方法进行直接修改可以有效地将销售人员从人数中排除。进行了全面的实验，以验证，评估和比较所提出的体系结构。结果证实了LRCN-RetailNet明显优于以前的RetailNet体系结构，后者仅限于每次迭代评估一个图像。和两个用于对象检测的最新神经网络。最后，计算性能实验证实，整个方法可以有效地实时估计人数。我们提出并证明，对方法进行直接修改可以有效地将销售人员从人数中排除。进行了全面的实验，以验证，评估和比较所提出的体系结构。结果证实了LRCN-RetailNet明显优于以前的RetailNet体系结构，后者仅限于每次迭代评估一个图像。和两个用于对象检测的最新神经网络。最后，计算性能实验证实，整个方法可以有效地实时估计人数。限于每次迭代评估一张图片；和两个用于对象检测的最新神经网络。最后，计算性能实验证实，整个方法可以有效地实时估计人数。限于每次迭代评估一张图片；和两个用于对象检测的最新神经网络。最后，计算性能实验证实，整个方法可以有效地实时估计人数。