Problem 2. Person Re-Identification. Person re-identification is the task of matching a detected person to a gallery of previously seen people, and determining their identity. In formulation, the...

Problem 2. Person Re-Identification. Person re-identification is the task of matching a detected person to a gallery of previously seen people, and determining their identity. In formulation, the problem is very similar to a typical biometrics task (where dimension reduction techniques such as PCA and/or LDA, or deep network methods using Siamese networks can be applied), however large changes in subject pose and their position relative to the camera, lighting, and occlusions make this a challenging task. Person re-identification (and performance for other retrieval tasks) is typically evaluated using Top-N accuracy and Cumulative Match Characteristic (CMC) curves. Top-N accuracy refers to the percentage of queries where the correct match is within the top N results. Ideally, the top result will always be the first (i.e. closest) returned match. A CMC curve plots the top-N accuracy for all possible values of N (from 1 to the number of unique IDs in the dataset). You have been provided with a portion of the Market-1501 dataset [1] (see Q2/Q2.zip, a widely used dataset for person re-identification. This data has been split into two segments: • Training: consists of the first 300 identities from Market-1501. Each identity has several images. In total, there are 5,933 colour images, each of size 128x64. • Testing: consists of a randomly selected pair of images from the final 301 identities. All images are colour, and of size 128x64. These images have been divided into two directories, Gallery and Probe, with one image from each ID in each directory. In using these datasets, you should use the Training dataset to train your model, and determine any model hyper-parameters. You may wish to further divide the Training set into training and validation to do this. To evaluate your model using the Testing data, you should transform images in both the Gallery and Probe into your chosen representations, and then for each image in the Probe set, compare it to each image in the Gallery and determine the index of the correct match. Your Task: Using this data, you are to: 1. Develop and evaluate a non-deep learning method for person re-identification. The method should be evaluated on the test set by considerin Top-1, Top-5 and Top-10 performance. A CMC (cumulative match characteristic) curve should also be provided. 2. Develop and evaluate a deep learning based method for person re-identification. The method should be evaluated on the test set by considering Top-1, Top-5 and Top-10 performance. A CMC (cumulative match characteristic) curve should also be provided. 3. Compare the performance of the two methods. Are there instances where the non-deep learning method works better? Comment on the respective strengths and weaknesses of the two approaches. In completing your answer you may also wish to consider the following: • You may wish to resize images to reduce computational burden. This is acceptable, but should be documented. Be mindful not to make images too small which will result in the loss of discriminative information and make identification very challenging.