A Siamese Neural Network is a type of neural network architecture designed for tasks involving similarity or dissimilarity measurement between pairs of input samples. The network consists of two identical subnetworks (twins) that share the same set of parameters and are trained simultaneously. The objective is to learn a representation such that similar inputs are mapped close to each other in the learned space, while dissimilar inputs are mapped farther apart. Siamese neural networks provide a powerful framework for learning similarity relationships in various domains. They are well-suited for tasks where pairwise comparisons are essential, and the goal is to discriminate between similar and dissimilar samples in the input space.


DeepTrack: Grouping RFID Tags Based on Spatio-temporal Proximity in Retail Spaces

RFID applications for taking inventory and processing transactions in point-of-sale (POS) systems improve operational efficiency but are not designed to provide insights about customers’ interactions with products. We bridge this gap by solving the proximity grouping problem to identify groups of RFID tags that stay in close proximity to each other over time. We design DeepTrack, a framework that uses deep learning to automatically track the group of items carried by a customer during her shopping journey. This unearths hidden purchase behaviors helping retailers make better business decisions and paves the way for innovative shopping experiences such as seamless checkout (‘a la Amazon Go). DeepTrack employs a recurrent neural network (RNN) with the attention mechanism, to solve the proximity grouping problem in noisy settings without explicitly localizing tags. We tailor DeepTrack’s design to track not only mobile groups (products carried by customers) but also flexibly identify stationary tag groups (products on shelves). The key attribute of DeepTrack is that it only uses readily available tag data from commercial off-the-shelf RFID equipment. Our experiments demonstrate that, with only two hours training data, DeepTrack achieves a grouping accuracy of 98.18% (99.79%) when tracking eight mobile (stationary) groups.