Gait-Assisted Person Re-identification in Wide Area Surveillance

Abstract

Gait has been shown to be an effective feature for person recognition and could be well suited for the problem of multi-frame person re-identification (Re-ID). However, person Re-ID poses very unique set of challenges, with changes in view angles and environments across cameras. Thus, the feature needs to be highly discriminative as well as robust to drastic variations to be effective for Re-ID. In this paper, we study the applicability of gait to person Re-ID when combined with color features. The combined features based Re-ID is tested for short period Re-ID on dataset we collected using 9 cameras and 40 IDs. Additionally, we also investigate the potential of gait features alone for Re-ID under real world surveillance conditions. This allows us to understand the potential of gait for long period Re-ID as well as under scenarios where color features cannot be leveraged. Both combined and gait-only features based Re-ID is tested on the publicly available, SAIVT SoftBio dataset. We select two popular gait features, namely Gait Energy Images (GEI) and Frame Difference Energy Images (FDEI) for Re-ID and propose a sparsified representation based gait recognition method.