@InProceedings{JrSilvVieiOliv:2019:DeLeAp,
               author = "Jr. , Val{\'e}rio Nogueira and Silva, Jos{\'e} Augusto and 
                         Vieira, Thales and Oliveira, Krerley",
          affiliation = "{Federal University of Alagoas (UFAL)} and {Federal University of 
                         Alagoas (UFAL)} and {Federal University of Alagoas (UFAL)} and 
                         {Federal University of Alagoas (UFAL)}",
                title = "RetailNet: A deep learning approach for people counting and hot 
                         spots detection in retail stores",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "deep learning, computer vision, people counting, crowd 
                         estimation.",
             abstract = "Customer behavior analysis is an essential issue for retailers, 
                         allowing for optimized store performance, enhanced customer 
                         experience, reduced operational costs, and consequently higher 
                         profitability. Nevertheless, not much attention has been given to 
                         computer vision approaches to automatically extract relevant 
                         information from images that could be of great value to retailers. 
                         In this paper, we present a low-cost deep learning approach to 
                         estimate the number of people in retail stores in real-time and to 
                         detect and visualize hot spots. For this purpose, only an 
                         inexpensive RGB camera, such as a surveillance camera, is 
                         required. To solve the people counting problem, we employ a 
                         supervised learning approach based on a Convolutional Neural 
                         Network (CNN) regression model. We also present a four channel 
                         image representation named RGBP image, composed of the 
                         conventional RGB image and an extra binary image P representing 
                         whether there is a visible person in each pixel of the image. To 
                         extract the latter information, we developed a 
                         foreground/background detection method that considers the 
                         peculiarities of people behavior in retail stores. The P image is 
                         also exploited to detect the hot spots of the store, which can 
                         later be visually analyzed. Several experiments were conducted to 
                         validate, evaluate and compare our approach using a dataset 
                         comprised of videos that were collected from a surveillance camera 
                         placed in a real shoe retail store. Results revealed that our 
                         approach is sufficiently robust to be used in real world 
                         situations and outperforms straightforward CNN approaches.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00029",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00029",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U2AEMS",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U2AEMS",
           targetfile = "cameraready.pdf",
        urlaccessdate = "2024, Apr. 28"
}