Appearance-based person re-identification by intra-camera discriminative models and rank aggregation

The main challenges in person re-identification are related to different camera acquisition conditions and high inter-class similarities. These aspects motivated us to handle such problems by learning intra-camera discriminative models, based on training samples, to discover representative individuals for a given sample (probe or gallery samples), referred to as prototypes. These prototypes are used to weight the features according to their discriminative power by using the Partial Least Square (PLS) method. We also exploit models built from the gallery and probe samples to generate re-identification results that will be combined in a single ranking using ranking aggregation techniques. According to the experiments, the proposed method achieves state-of-the-art results. They also demonstrate that aggregating the results achieved by our method with results achieved by a distance metric learning method, outperforms the state-of-the-art, e.g., the top-1 rank is increased in almost 10 percent points for VIPeR and PRID 450S data sets.