Abstract
In this paper, we propose a deep spatio-temporal appearance (DSTA) descriptor for person re-identification (re-ID). The proposed descriptor is based on the deep Fisher vector (FV) encoding of the trajectory spatio-temporal structures. These have the advantage of robustly handling the misalignment in the pedestrian tracklets. The deep encoding exploits the richness of the spatio-temporal structural information around the trajectories. This is achieved by hierarchically encoding the trajectory structures leveraging a larger tracklet neighborhood scale when moving from one layer to the next one. In order to eliminate the noisy background located around the pedestrian and model the uniqueness of its identity, the deep FV encoder is further enriched towards the deep Salient-Gaussian weighted FV (deepSGFV) encoder by integrating the pedestrian Gaussian and saliency templates in the encoding process, respectively. The proposed descriptor produces competitive accuracy with respect to state-of-the art methods and especially the deep CNN ones without necessitating either pre-training or data augmentation on four challenging pedestrian video datasets: PRID2011, i-LIDS-VID, Mars and LPW. The further combination of DSTA with deep CNN boosts the current state-of-the-art methods and demonstrates their complementarity.