Appearance Descriptors for Person Re-identification: a Comprehensive Review
It provides a structured overview for researchers in video surveillance, but it is incremental as it synthesizes existing knowledge without introducing new methods.
This paper reviews current approaches for building appearance descriptors in person re-identification, categorizing techniques by body models and features to address the challenge of recognizing individuals across camera networks using clothing appearance.
In video-surveillance, person re-identification is the task of recognising whether an individual has already been observed over a network of cameras. Typically, this is achieved by exploiting the clothing appearance, as classical biometric traits like the face are impractical in real-world video surveillance scenarios. Clothing appearance is represented by means of low-level \textit{local} and/or \textit{global} features of the image, usually extracted according to some part-based body model to treat different body parts (e.g. torso and legs) independently. This paper provides a comprehensive review of current approaches to build appearance descriptors for person re-identification. The most relevant techniques are described in detail, and categorised according to the body models and features used. The aim of this work is to provide a structured body of knowledge and a starting point for researchers willing to conduct novel investigations on this challenging topic.