# Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector
## 2 Related Work  
### Person Re-Identification  
Nowadays, person re-identification represents one of the primary purposes in the field of video processing. The re-identification of a person is the process of associating a particular person caught on a surveillance camera with other appearances of the same person, taken with other cameras, or at other time instances. The main goal of person re-identification is to assign unique labels to each person that appears on one or more surveillance cameras in order to recreate lost tracks and create an itinerary of each person inside a multi-camera surveillance network [11]. Person re-identification encounters several difficulties. First, the main challenge is represented by intra-class variations. The same person can be caught in many poses, wearing different clothes, in different colors. Thus, the features vector extracted at the network's output will be quite different in such situations, making their association extremely challenging. Another aspect that influences the re-identification process is the variation of light. Light intensity, shadows, reflected light, or artificial lighting can cause a subject to appear differently on a surveillance camera. **Existing re-identification datasets. ** In the last decade, increasing databases have been created for re-identification. Each database is different in terms of the locations, the number of identities or appearances, and the number of images that make it up. The main difference is the way bounding boxes are generated. These are either generated manually, which is the case of VIPeR [12] or CUHK02 [13] datasets, either generated automatically, while using a detector, such as CUHK03 [14], Market-1501 [15], MARS [16], or DukeMTMC-reID [17] datasets. These are the most familiar and utilized databases in the existing methods in the current literature. Two databases less used, in both training and testing, a re-identification model are RPIField [18] and PRW-v16.04.20 [19] datasets. Table 1 describes all of these databases. **Existing methods. ** In general, in most of the existing works, the re-identification problem is treated as a recognition problem. Some other methods are based on appearance [20], but these features are not stable over a long period, because people dress differently from a day to another. The greatest evolution in the field of re-identification is closely related to deep learning, because it is known that the use of neural networks leads to the highest performance [21]. The main difference between the existing re-identification methods is related to how this problem is addressed: either as a classification problem or as a more general one, which is based on deep metric embedding learning. The first implemented versions treat re-identification as an extension of the classification method. One approach uses the Softmax function to decide whether or not two input images belong to the same person [14; 22]. In order to improve the Softmax function, which does not take the distance into account, the idea of the logistic regression loss function was introduced in [23], which improves the ability of the network to learn features. Later, ref. [24] proposed a Gaussian-based Softmax function, which also takes into account the distribution of features (which is considered a Gaussian distribution). Recently, a newer approach is to implement Siamese networks [25]. Although it is also a classification method, unlike the other methods, this one is more efficient when the number of classes for people's recognition increases. The most used approach is to implement a deep metric embedding. In the re-identification methods, the most commonly related function is triplet loss [26]. This function is also used in the current paper. Over time, various approaches have been implemented on this topic, e.g., some papers have implemented new methods for generating several triplet types [27; 28; 29], while others have tried to improve the triplet loss function [30; 31; 32].