Identification of pedestrian attributes using deep network
Laksono Kurnianggoro, Kang-Hyun Jo · IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society · 2017
In the person re-identification across multiple camera research field, attributes of the pedestrian are important cues to differentiate the appearance of each identity. In this work, ten types of attributes are considered as defined in the DukeMTMC-attribute dataset. A custom deep network architecture is proposed to perform the identification process. Furthermore, experiments were carried out to assess the system compared to other pre-trained networks which are commonly used in other literature. The results show that the proposed network achieve better performance compared to the others.σ