# Re-Identification in Urban Scenarios: A Review of Tools and Methods
###### Abstract  
With the widespread use of surveillance image cameras and enhanced awareness of public security, objects, and persons Re-Identification (ReID), the task of recognizing objects in non-overlapping camera networks has attracted particular attention in computer vision and pattern recognition communities. Given an image or video of an object-of-interest (query), object identification aims to identify the object from images or video feed taken from different cameras. After many years of great effort, object ReID remains a notably challenging task. The main reason is that an object's appearance may dramatically change across camera views due to significant variations in illumination, poses or viewpoints, or even cluttered backgrounds. With the advent of Deep Neural Networks (DNN), there have been many proposals for different network architectures achieving high-performance levels. With the aim of identifying the most promising methods for ReID for future robust implementations, a review study is presented, mainly focusing on the person and multi-object ReID and auxiliary methods for image enhancement. Such methods are crucial for robust object ReID, while highlighting limitations of the identified methods. This is a very active field, evidenced by the dates of the publications found. However, most works use data from very different datasets and genres, which presents an obstacle to wide generalized DNN model training and usage. Although the model's performance has achieved satisfactory results on particular datasets, a particular trend was observed in the use of 3D Convolutional Neural Networks (CNN), attention mechanisms to capture object-relevant features, and generative adversarial training to overcome data limitations. However, there is still room for improvement, namely in using images from urban scenarios among anonymized images to comply with public privacy legislation. The main challenges that remain in the ReID field, and prospects for future research directions towards ReID in dense urban scenarios, are also discussed. person ReID; computer vision; deep neural networks; image enhancement +
Footnote †: journal: Medical  
The former category focuses on matching a probe image of one object, with an image of the object with the same ID among gallery sets, which is mainly established based on image content analysis and matching. In contrast, the latter category focuses on matching two videos, exploiting different information, such as temporal, and motion-based information. A gallery corresponds to a collection of object images gathered from different perspectives over time. In both approaches, the pairs of objects to be matched are analogous. However, in real scenarios, object ReID needs to be conducted between the image and video. For example, given a picture of a criminal suspect, the police would like to quickly locate and track the suspect from hundreds of city surveillance videos. The ReID under this scenario is called image-to-video person ReID, where a probe image is searched in a gallery of videos acquired from different surveillance cameras. Although videos contain more information, image-to-video ReID share the same challenges with image-based and video-based objects ReID, namely, similar appearance, low resolution, substantial variation in poses, occlusion, and different viewpoints. In addition, an extra difficulty resides on the match between two different datasets, one static and another dynamic, i.e., image and video, respectively. Image and video are usually represented using different features. While only visual features can be obtained from a single image, both visual features and spatial-temporal features can be extracted from a video. Recently, CNN has shown potential for learning state-of-the-art image feature embedding [3; 4] and Recurrent Neural Network (RNN) yields a promising performance in obtaining spatial-temporal features from videos [5; 6]. In general, there are two major types of deep learning structures for object ReID; namely, verification models and identification models.