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# Formulation of the Addresssed Machine Learning Problem
## Probelm Definition
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, 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
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:
$$ NN(x) \operatorname*{argmin}_{i\in[m]} \|x-x_i\|^2 $$
*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
\begin{figure}
\begin{center}
\includegraphics[width=20em]{fig/baseline.pdf}
\caption{Top K Accuracy for Nearest Neighbour classification}
\label{fig:baselineacc}
\end{center}
\end{figure}
\begin{figure}
\begin{center}
\includegraphics[width=22em]{fig/eucranklist.png}
\caption{Top 10 ranklist for 5 probes}
\label{fig:eucrank}
\end{center}
\end{figure}
# Suggested Improvement
\begin{figure}
\begin{center}
\includegraphics[width=24em]{fig/ranklist.png}
\caption{Top 10 ranklist (improved method) 5 probes}
\label{fig:ranklist2}
\end{center}
\end{figure}
TODO:
~~
s/kNN/NN/
~~
\begin{figure}
\begin{center}
\includegraphics[width=20em]{fig/comparison.pdf}
\caption{Top K Accurarcy}
\label{fig:baselineacc}
\end{center}
\end{figure}
\begin{figure}
\begin{center}
\includegraphics[width=17em]{fig/pqvals.pdf}
\caption{Top 1 Accuracy when k1 and k2}
\label{fig:pqvals}
\end{center}
\end{figure}
# Conclusion
# References
# Appendix
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