1,374 research outputs found
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
Re-ID done right: towards good practices for person re-identification
Training a deep architecture using a ranking loss has become standard for the
person re-identification task. Increasingly, these deep architectures include
additional components that leverage part detections, attribute predictions,
pose estimators and other auxiliary information, in order to more effectively
localize and align discriminative image regions. In this paper we adopt a
different approach and carefully design each component of a simple deep
architecture and, critically, the strategy for training it effectively for
person re-identification. We extensively evaluate each design choice, leading
to a list of good practices for person re-identification. By following these
practices, our approach outperforms the state of the art, including more
complex methods with auxiliary components, by large margins on four benchmark
datasets. We also provide a qualitative analysis of our trained representation
which indicates that, while compact, it is able to capture information from
localized and discriminative regions, in a manner akin to an implicit attention
mechanism
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