# Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector
## 1 Introduction  
Nowadays, the surrounding world is full of surveillance cameras, so that data that are generated from video surveillance are continuously expanding. Therefore, it has become impossible for a human operator to track and verify people's identities in the entire database on its own. For this reason, the tendency is to use systems that are capable of achieving the recognition [1, 2] and tracking [3, 4] of people automatically. Automatizing in video processing was possible due to the recent evolution in this field. New methods were implemented, being able to offer more remarkable performances. Simultaneously, hardware components had evolved and they can achieve higher levels of parallel processing. Most algorithms in the field focus on detecting and recognizing people [5]. Although it faces many obstacles, the recognition has evolved considerably over the last years. When compared to face recognition, a more powerful application is the person re-identification [6]. Implementing a person re-identification system is one of the most difficult and challenging tasks in video processing. This field is continually expanding, while newer methods are being developed at an ever more accelerated pace. Furthermore, nowadays, the computational power is high, while thehardware resources are increasingly proficient. However, the problem of person re-identification has not yet been solved. This task consists of finding a match of a given image inside the database. It uses facial features and other features that are found throughout the person's body, such as clothing, colors, height, and many others. The importance of person re-identification can be observed in many situations. First, facial recognition only works successfully if the subject is close enough to the camera and placed facing it. However, there are many situations where it may occur partial or even total occlusions of the faces. Furthermore, it may happen that a particular person deliberately covers the face or hides it from the surveillance cameras. In such cases, facial recognition cannot be performed. If a precise and accurate re-identification method can be implemented, then the same person's appearances can be tracked in different places in order to create an itinerary of a person of interest [7]. In this way, a person can be recognized even when the face is hidden. Thus, person recognition can be done by associating a person with other appearances (from other places) through re-identification. This application has several real-life applications, but the most important is personal and national security, by identifying a suspect person, even when the face is hidden. This is the main reason why more and more ideas are being developed to address person re-identification. People can easily re-identify other people by considering a person's face, weight, height, facial features, how they are dressed, or even how they walk. Nevertheless, it is challenging to implement an algorithm that is capable of doing the same thing automatically, which is why this problem is continuously being researched. The majority of the existing re-identification methods offer outstanding results and high performances when using the same distribution datasets. When changes in location, environment, background, and other characteristic factors of a particular distribution occur, the problem of re-identification is considerably more difficult. In this paper, we target to address this problem by presenting a first attempt to obtain a general re-identification system, which is robust in terms of distribution variations. Our re-identification method is based on the You Only Look Once (YOLO) model [8], which uses the Convolutional Neural Network (CNN) Darknet-53 [9; 10]. The original purpose of the YOLO model is object detection. Our proposal is to modify the network architecture. The first goal was to adapt the network in order to recognize instances of the same class by creating a recognition system [2].