## 2 Related Work
First, the base CNN model's stem part is used to generate convolutional feature maps from the entire gallery image. Next, the pedestrian proposal net utilizes these feature maps to detect candidate pedestrians. Soft-max classifier and linear regression are used to predict actual pedestrians and their locations, respectively. The actual pedestrians are then provided to the identification net for feature extraction. A novel Online Instance Matching loss function is incorporated to guide the training of the re-identification net. The people in the gallery are ranked according to their distances from the target person. The authors collected and annotated a large-scale dataset from streets via hand-held cameras as well as movies. The performance of [PERSON] et al. [30] outperformed the preceding set of algorithms. Inspired by the advantages of Siamese architecture [31], [PERSON] et al. [32] developed a Feature Distilling Generative Adversarial Network (FD-GAN), which is composed of image encoder, image generator, identity verifier, and identity as well as pose adversarial discriminators. First, the image encoder, employing ResNet50 [23], extracts features from the input image at each branch. Next, the identity verifier monitors the learning process for the re-identification task. It determines the similarity of the input images. The identity discriminator is designed to retain identity-related information, whereas the pose discriminator is trained to eradicate pose-related information from the extracted features. Similarly, the fundamental concept of FD-GAN is to learn identity-related features while restrictingthe system not to rely on pose-related features. The authors utilized cross-entropy loss, adversarial losses of identity and pose discriminators, and reconstruction loss. Multiple Granularity Network (MGN) [33] is a deep architecture consisting of three independent branches (subnets) with ResNet50 [23] as a backbone. The branches are named based on the type of features it learns, i.e., the upper branch is called Global Branch as it captures global features, whereas the lower two branches learn local features from the input image. During the parts-based feature extraction, the input image is split into three horizontal stripes. The authors employed softmax and triplet loss functions for classification and metric learning, respectively. [PERSON] et al. [34] proposed an exemplar memory-based deep network that utilizes pre-trained ResNet50 [23] as backbone. The authors added a fully connected layer after the pooling-5 layer of ResNet50 followed by batch normalization, ReLU activation, Dropout, and two more components. The first component performs supervised learning from labeled data, whereas the second component performs invariance learning from unlabelled data. The exemplar-memory concept is introduced to store the output features of a fully connected layer for all previously seen images from the target domain. Exemplar-memory helps achieve generalization capability concerning exemplar-invariance, camera-invariance, and neighborhood-invariance of the target domain images. The proposed deep network outperformed the existing state-of-the-art approaches based on unsupervised domain adaptation. The recently-reveloped, Omni-scale network (OSNet) [35] explicitly learns multi-scale features at each layer of the network. The basic building blocks of the network contain multiple convolutional streams capable of extracting features at various scales. The resulting feature maps are combined through a unified aggregation gate. The authors demonstrated that features extracted from local regions and the entire body are equally important for the person's re-ID task. The OSNet [35] performance has been analyzed on six datasets. The results validated its superiority over existing state-of-the-art techniques.