  In video-based person re-identification, both the spatial and temporal
features are known to provide orthogonal cues to effective representations.
Such representations are currently typically obtained by aggregating the
frame-level features using max/avg pooling, at different points of the models.
However, such operations also decrease the amount of discriminating information
available, which is particularly hazardous in case of poor separability between
the different classes. To alleviate this problem, this paper introduces a
symbolic temporal pooling method, where frame-level features are represented in
the distribution valued symbolic form, yielding from fitting an Empirical
Cumulative Distribution Function (ECDF) to each feature. Also, considering that
the original triplet loss formulation cannot be applied directly to this kind
of representations, we introduce a symbolic triplet loss function that infers
the similarity between two symbolic objects. Having carried out an extensive
empirical evaluation of the proposed solution against the state-of-the-art, in
four well known data sets (MARS, iLIDS-VID, PRID2011 and P-DESTRE), the
observed results point for consistent improvements in performance over the
previous best performing techniques.
