12 research outputs found
Pose-Normalized Image Generation for Person Re-identification
Person Re-identification (re-id) faces two major challenges: the lack of
cross-view paired training data and learning discriminative identity-sensitive
and view-invariant features in the presence of large pose variations. In this
work, we address both problems by proposing a novel deep person image
generation model for synthesizing realistic person images conditional on the
pose. The model is based on a generative adversarial network (GAN) designed
specifically for pose normalization in re-id, thus termed pose-normalization
GAN (PN-GAN). With the synthesized images, we can learn a new type of deep
re-id feature free of the influence of pose variations. We show that this
feature is strong on its own and complementary to features learned with the
original images. Importantly, under the transfer learning setting, we show that
our model generalizes well to any new re-id dataset without the need for
collecting any training data for model fine-tuning. The model thus has the
potential to make re-id model truly scalable.Comment: 10 pages, 5 figure
Clip-level feature aggregation : a key factor for video-based person re-identification
In the task of video-based person re-identification, features
of persons in the query and gallery sets are compared to search the
best match. Generally, most existing methods aggregate the frame-level
features together using a temporal method to generate the clip-level fea-
tures, instead of the sequence-level representations. In this paper, we
propose a new method that aggregates the clip-level features to obtain
the sequence-level representations of persons, which consists of two parts,
i.e., Average Aggregation Strategy (AAS) and Raw Feature Utilization
(RFU). AAS makes use of all frames in a video sequence to generate
a better representation of a person, while RFU investigates how batch
normalization operation influences feature representations in person re-
identification. The experimental results demonstrate that our method
can boost the performance of existing models for better accuracy. In
particular, we achieve 87.7% rank-1 and 82.3% mAP on MARS dataset
without any post-processing procedure, which outperforms the existing
state-of-the-art