Person re-identification consists of recognizing individuals across different sensors of a camera
network. Whereas clothing appearance cues are widely used, other modalities could
be exploited as additional information sources, like anthropometric measures and gait. In
this work we investigate whether the re-identification accuracy of clothing appearance descriptors
can be improved by fusing them with anthropometric measures extracted from
depth data, using RGB-Dsensors, in unconstrained settings. We also propose a dissimilaritybased
framework for building and fusing multi-modal descriptors of pedestrian images for
re-identification tasks, as an alternative to the widely used score-level fusion. The experimental
evaluation is carried out on two data sets including RGB-D data, one of which is a
novel, publicly available data set that we acquired using Kinect sensors.
In this dissertation we also consider a related task, named semantic retrieval of pedestrians
in video surveillance scenarios, which consists of searching images of individuals using
a textual description of clothing appearance as a query, given by a Boolean combination of
predefined attributes. This can be useful in applications like forensic video analysis, where
the query can be obtained froma eyewitness report. We propose a general method for implementing
semantic retrieval as an extension of a given re-identification system that uses any
multiple part-multiple component appearance descriptor. Additionally, we investigate on
deep learning techniques to improve both the accuracy of attribute detectors and generalization
capabilities. Finally, we experimentally evaluate our methods on several benchmark
datasets originally built for re-identification task
