Context-Aware Person Re-Identification in the Wild Via Fusion of Gait and Anthropometric Features

In this work, we present a context-aware ensemble fusion framework based on soft-biometric features, for long term person re-identification (Re-ID) in wild surveillance scenarios. The characteristics of a person that best correlate to its identity depend strongly on the view point. For instance, a person with a short stride gait is better perceived from a lateral view, whereas a person with a large chest is more distinct from a frontal view. Thus we associate context to the viewing direction of walking people in a surveillance scenario and choose the best features for each case. Using the MS KinectTM sensor v.2, we collect data from walking subjects and extract associated anthropometric and gait features. Each context is analysed with a Feature selection technique (Sequential Forward Selection) so that only the most relevant features for the context are retained. Then, individual context-specific classifiers are trained leveraging those selected features. Finally, we propose a contextaware ensemble fusion strategy, which we term as 'Contextspecific score-level fusion', based on the adaptive weighted sum of the results of individual classifiers. The proposed contextaware Re-ID framework demonstrate significant performance improvement both in terms of speed (up to 4.5 times faster) and accuracy (up to 17% rank-1 Re-ID rate) compared to the Context-unaware systems. From the study, we show that gait features are better for lateral views and anthropometric features are better for frontal views, confirming the results of previous studies.