2 research outputs found
Video-based Person Re-identification Using Spatial-Temporal Attention Networks
We consider the problem of video-based person re-identification. The goal is
to identify a person from videos captured under different cameras. In this
paper, we propose an efficient spatial-temporal attention based model for
person re-identification from videos. Our method generates an attention score
for each frame based on frame-level features. The attention scores of all
frames in a video are used to produce a weighted feature vector for the input
video. Unlike most existing deep learning methods that use global
representation, our approach focuses on attention scores. Extensive experiments
on two benchmark datasets demonstrate that our method achieves the
state-of-the-art performance. This is a technical report
A Symbolic Temporal Pooling method for Video-based Person Re-Identification
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.Comment: 11 page