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diff --git a/report2/paper.md b/report2/paper.md index 2e0bb0a..4ccf5ca 100755 --- a/report2/paper.md +++ b/report2/paper.md @@ -1,25 +1,17 @@ -# Summary - -In this report we analysed how distance metrics learning affects classification -accuracy for the dataset CUHK03. The baseline method used for classification is -Nearest Neighbors based on Euclidean distance. The improved approach we propose -mixes Jaccardian and Mahalanobis metrics to obtain a ranklist that takes into -account also the reciprocal neighbors. This approach is computationally more -complex, since the matrices representing distances are effectively calculated -twice. However it is possible to observe a significant accuracy improvement of -around 10% for the $@rank1$ case. Accuracy improves overall, especially for -$@rankn$ cases with low n. + +# Forulation 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. - -## CUHK03 +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 Summary The dataset CUHK03 contains 14096 pictures of people captured from two different cameras. The feature vectors used, extracted from a trained ResNet50 model |