2 research outputs found
Adaptive Deep Metric Embeddings for Person Re-Identification under Occlusions
Person re-identification (ReID) under occlusions is a challenging problem in
video surveillance. Most of existing person ReID methods take advantage of
local features to deal with occlusions. However, these methods usually
independently extract features from the local regions of an image without
considering the relationship among different local regions. In this paper, we
propose a novel person ReID method, which learns the spatial dependencies
between the local regions and extracts the discriminative feature
representation of the pedestrian image based on Long Short-Term Memory (LSTM),
dealing with the problem of occlusions. In particular, we propose a novel loss
(termed the adaptive nearest neighbor loss) based on the classification
uncertainty to effectively reduce intra-class variations while enlarging
inter-class differences within the adaptive neighborhood of the sample. The
proposed loss enables the deep neural network to adaptively learn
discriminative metric embeddings, which significantly improve the
generalization capability of recognizing unseen person identities. Extensive
comparative evaluations on challenging person ReID datasets demonstrate the
significantly improved performance of the proposed method compared with several
state-of-the-art methods.Comment: 6 pages, 3 figure
Person Re-Identification using Deep Learning Networks: A Systematic Review
Person re-identification has received a lot of attention from the research
community in recent times. Due to its vital role in security based
applications, person re-identification lies at the heart of research relevant
to tracking robberies, preventing terrorist attacks and other security critical
events. While the last decade has seen tremendous growth in re-id approaches,
very little review literature exists to comprehend and summarize this progress.
This review deals with the latest state-of-the-art deep learning based
approaches for person re-identification. While the few existing re-id review
works have analysed re-id techniques from a singular aspect, this review
evaluates numerous re-id techniques from multiple deep learning aspects such as
deep architecture types, common Re-Id challenges (variation in pose, lightning,
view, scale, partial or complete occlusion, background clutter), multi-modal
Re-Id, cross-domain Re-Id challenges, metric learning approaches and video
Re-Id contributions. This review also includes several re-id benchmarks
collected over the years, describing their characteristics, specifications and
top re-id results obtained on them. The inclusion of the latest deep re-id
works makes this a significant contribution to the re-id literature. Lastly,
the conclusion and future directions are included.Comment: 34 pages, 15 figure