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 ## Probelm Definition
 
-The problem to solve is to create a ranklist for each image of the query set
-by finding the nearest neighbor(s) within a gallery set. However gallery images
-with the same label and taken from the same camera as the query image should
-not be considered when forming the ranklist.
+The person re-identification problem presented in this paper requires mtatching pedestrian images from disjoint camera's by pedestrian detectors. This problem is challenging, as identities captured in photsos are subject to various lighting, pose, blur, background and oclusion from various camera views. This report considers features extracted from the CUHK03 dataset, following a 50 layer Residual network (Resnet50),. This paper considers distance metrics techniques which can be used to perform person re-identification across **disjoint* cameras, using these features.
 
 ## Dataset - CUHK03
 
 The dataset CUHK03 contains 14096 pictures of people captured from two 
-different cameras. The feature vectors used come from passing the
-rescaled images through ResNet50. Each feature vector contains 2048
-features that we use for classification. The pictures represent 1467 different
-people and each of them appears between 9 and 10 times. The separation of
-train_idx, query_idx and gallery_idx allows to perform taining and validation 
-on a training set (train_idx, adequately split between test, train and 
-validation keeping the same number of identities). This prevents overfitting 
-the algorithm to the specific data associated with query_idx and gallery_idx.
+different cameras. The feature vectors used, extracted from a trained ResNet50 model
+, contain 2048 features that are used for identification.
+
+The pictures represent 1467 different
+identities, each of which appears 9 to 10 times. Data is seperated in train, query and gallery sets with `train_idx`, `query_idx` and `gallery_idx` respectively, where the training set has been used to develop the ResNet50 model used for feature extraction. This procedure has allowed the evaluation of distance metric learning techniques on the query and gallery sets, without an overfit feature set a the set, as it was explicitly trained on the training set.
 
 ## Nearest Neighbor ranklist
 
@@ -25,10 +19,13 @@ Nearest Neighbor aims to find the gallery image whose feature are the closest to
 the ones of a query image, predicting the class of the query image as the same 
 of its nearest neighbor(s). The distance between images can be calculated through
 different distance metrics, however one of the most commonly used is euclidean
-distance, represented as $d=\sqrt{\sum (x-y)^{2}}$.
+distance:
+
+$$ NN(x) \operatorname*{argmin}_{i\in[m]} \|x-x_i\|^2 $$
 
-EXPLAIN KNN BRIEFLY
+*Square root when calculating euclidean distance is ommited as it does not affect ranking by distance*
 
+Alternative distance metrics exist such as jaccardian and mahalanobis, which can be used as an alternative to euclidiean distance.
 
 # Baseline Evaluation