  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 a state-of-the-art
neural network for object detection. Finally, computational performance
experiments confirmed that the entire methodology is effective to estimate
people count in real-time.
