Autoencoder Ensemble for Person Re-Identification
Kajal Kansal, A. Venkata Subramanyam · 2019
Person re-identification (re-id) aims to match people from non-overlapping multi-camera networks. Recently, with the advancement of deep learning techniques, the performance of re-id has been improved swiftly. However, most of the existing re-id methods need large number of samples for training due to which the models do not generalize well on smaller datasets and suffers from small sample size problem. Additionally, they focus on single scale appearance information while ignoring rich information that can be exploited from other scales. In this paper, we propose a simple yet effective autoencoder, comprising of an encoder and a sequential decoder. The goal of the network is two fold. First, the network learns features by introducing a generative task to the embedding layer so that it can make features more generalizable to the unknown test data to prevent from overfitting. Second, the encoder feature embedding is used as an input to decoder to reconstruct the input image with various scales to achieve robustness against scale variations. The effectiveness of our proposed method is validated on three public person re-identification datasets, Market-1501, DukeMTMC-reID and CUHK03.