# Multi-Camera Person Re-Identification Based on Trajectory Data
## 1 Introduction  
Retail companies have been going through adaptation processes due to the digitalization that has occurred over the last few years, along with increasing consumer demands. As a result, large retailers are increasingly seeking to understand customer behaviors to predict and meet their needs and, consequently, increase sales. With the increase in the amount of data that can be collected in these stores, such as images from video surveillance cameras, and thanks to scientific advances in the field of computer vision, it is now possible to identify individuals in a series of consecutive video frames. This allows for the extraction of the path these individuals take during their time in the store. Predicting the trajectory a customer takes and analyzing their behavior using factors such as the time spent in the store, the areas visited, or their walking speed are some of the potential applications derived from these customer trajectory data [1]. For these applications to be effective, it is necessary that the data used are as accurate as possible to the customer's real trajectory. Achieving this can be challenging with just computer vision techniques due to obstructions in the camera's view (e.g., objects), poor lighting conditions, or the complexities of re-identifying an object or individual across multiple cameras. Several authors tried to address some of these specific concerns. For instance, ref. [2] proposed an algorithm to identify and track targets captured in images across multiple cameras. They employed B-spline polynomials to approximate and define target trajectories, providing a smoother representation of rapidly moving targets. The method then uses homography transformations to compare target trajectories from different cameras, re-identifying corresponding targets based on trajectory similarities. This approachrelies on finding similarities in the trajectories across different cameras to re-identify individuals. Similarly, ref. [3] created a dataset for multi-target multi-camera tracking (MTMCT) derived from the video game GTA V. The dataset, entitled Multi Camera Track Auto (MTA), comprises videos recorded on six different cameras, each with more than 100 min of footage, showcasing more than 2800 bots (individual identities). Beyond creating the dataset, they proposed a system for bot detecting, re-identification, and tracking, further calculating the distances they traveled and associating trajectories. The process of associating trajectories adopts a clustering methodology, grouping bot trajectories based on distance measures. These measures are calculated using weighted aggregation of five individual distances or constraints, encompassing factors such as the presence of tracks on one or more cameras, homography matching distance, linear prediction discounts, and appearance features. The work of [4] is also related. They proposed a method for tracking people in crowded scenes using multiple cameras to address the occlusions. The method focused on the detection of individuals' heads to extract their trajectory. As such, there is no need to identify the entire body to detect the presence of a person in a specific location. However, this approach might face challenges in situations where individuals' faces need to be anonymized for privacy reasons, making the detection more complex. The authors in [5] introduced a system designed for real-time re-identification of individuals by leveraging trajectory prediction. To predict a person's movement, it uses a method that generates an image trajectory based on visual data and image coordinates. This approach differs from ours in the way it uses image data to make the re-identification. In another study, ref. [6] proposed a method to detect individuals via surveillance cameras, generating a heatmap depicting the most visited locations within a store. Their detection algorithm employed YOLOv5 complemented by a homography matrix to represent the projection coordinates of individuals inside the store.