Unsupervised Person Re-ID in Surveillance Feed Using Re-ranking

With the increase of video feeds from the network of surveillance cameras and available sophisticated detection and bounding box techniques we have seen a jump in the use of deep learning models in the past years. These Deep Neural Network models are Supervised, in the sense that they require large labeled data samples. This hunger of more and more labeled data can be removed by moving on to Unsupervised learning. Despite of significant progress, very less attention is paid to unsupervised techniques. In our approach we tries to improve the accuracy of the Unsupervised Neural Network model by using re-ranking. K-mean algorithm is used to obtain the initial cluster as the samples are unlabeled initially and k-reciprocal nearest neighbor method is used to re-rank the output to remove false matches. Experiments are performed on DUKE and CUHK01 datasets.

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