Late Fusion in Part-based Person Re-identification

In person re-identification, the purpose is to match persons across, typically, non-overlapping cameras. This introduces challenges such as occlusion and changes in view and lighting. In order to overcome these challenges, discriminative features are extracted and used in combination with a supervised metric learning algorithm. Most often, feature representations are created from the entire body, causing noisy features if certain parts are occluded. Therefore, we propose a system which applies the same learning algorithm separately on feature representations from different body parts and late fuses the outputs, to take advantage of situations in which features from certain body parts are more discriminative than other. By evaluation on features at three abstraction levels, we show that the proposed system increase accuracy by up to 19.87% in the case of high-level features. In addition, we also fuse the features at different abstraction levels to further improve results. Experimental results on VIPeR and CUHK03 show similar performance to state-of-the-art with rank-1 accuracies of 52.72% and 61.50%, respectively, while results on the datasets PRID450S and CUHK01 show rank-1 accuracies of 78.36% and 73.40%, respectively, improvements of 11.74% and 7.76% compared to state-of-the-art.

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