977 research outputs found
Holistic Guidance for Occluded Person Re-Identification
In real-world video surveillance applications, person re-identification
(ReID) suffers from the effects of occlusions and detection errors. Despite
recent advances, occlusions continue to corrupt the features extracted by
state-of-art CNN backbones, and thereby deteriorate the accuracy of ReID
systems. To address this issue, methods in the literature use an additional
costly process such as pose estimation, where pose maps provide supervision to
exclude occluded regions. In contrast, we introduce a novel Holistic Guidance
(HG) method that relies only on person identity labels, and on the distribution
of pairwise matching distances of datasets to alleviate the problem of
occlusion, without requiring additional supervision. Hence, our proposed
student-teacher framework is trained to address the occlusion problem by
matching the distributions of between- and within-class distances (DCDs) of
occluded samples with that of holistic (non-occluded) samples, thereby using
the latter as a soft labeled reference to learn well separated DCDs. This
approach is supported by our empirical study where the distribution of between-
and within-class distances between images have more overlap in occluded than
holistic datasets. In particular, features extracted from both datasets are
jointly learned using the student model to produce an attention map that allows
separating visible regions from occluded ones. In addition to this, a joint
generative-discriminative backbone is trained with a denoising autoencoder,
allowing the system to self-recover from occlusions. Extensive experiments on
several challenging public datasets indicate that the proposed approach can
outperform state-of-the-art methods on both occluded and holistic datasetsComment: 10 page
Body Part-Based Representation Learning for Occluded Person Re-Identification
Occluded person re-identification (ReID) is a person retrieval task which
aims at matching occluded person images with holistic ones. For addressing
occluded ReID, part-based methods have been shown beneficial as they offer
fine-grained information and are well suited to represent partially visible
human bodies. However, training a part-based model is a challenging task for
two reasons. Firstly, individual body part appearance is not as discriminative
as global appearance (two distinct IDs might have the same local appearance),
this means standard ReID training objectives using identity labels are not
adapted to local feature learning. Secondly, ReID datasets are not provided
with human topographical annotations. In this work, we propose BPBreID, a body
part-based ReID model for solving the above issues. We first design two modules
for predicting body part attention maps and producing body part-based features
of the ReID target. We then propose GiLt, a novel training scheme for learning
part-based representations that is robust to occlusions and non-discriminative
local appearance. Extensive experiments on popular holistic and occluded
datasets show the effectiveness of our proposed method, which outperforms
state-of-the-art methods by 0.7% mAP and 5.6% rank-1 accuracy on the
challenging Occluded-Duke dataset. Our code is available at
https://github.com/VlSomers/bpbreid
DROP: Decouple Re-Identification and Human Parsing with Task-specific Features for Occluded Person Re-identification
The paper introduces the Decouple Re-identificatiOn and human Parsing (DROP)
method for occluded person re-identification (ReID). Unlike mainstream
approaches using global features for simultaneous multi-task learning of ReID
and human parsing, or relying on semantic information for attention guidance,
DROP argues that the inferior performance of the former is due to distinct
granularity requirements for ReID and human parsing features. ReID focuses on
instance part-level differences between pedestrian parts, while human parsing
centers on semantic spatial context, reflecting the internal structure of the
human body. To address this, DROP decouples features for ReID and human
parsing, proposing detail-preserving upsampling to combine varying resolution
feature maps. Parsing-specific features for human parsing are decoupled, and
human position information is exclusively added to the human parsing branch. In
the ReID branch, a part-aware compactness loss is introduced to enhance
instance-level part differences. Experimental results highlight the efficacy of
DROP, especially achieving a Rank-1 accuracy of 76.8% on Occluded-Duke,
surpassing two mainstream methods. The codebase is accessible at
https://github.com/shuguang-52/DROP
Dynamic Prototype Mask for Occluded Person Re-Identification
Although person re-identification has achieved an impressive improvement in
recent years, the common occlusion case caused by different obstacles is still
an unsettled issue in real application scenarios. Existing methods mainly
address this issue by employing body clues provided by an extra network to
distinguish the visible part. Nevertheless, the inevitable domain gap between
the assistant model and the ReID datasets has highly increased the difficulty
to obtain an effective and efficient model. To escape from the extra
pre-trained networks and achieve an automatic alignment in an end-to-end
trainable network, we propose a novel Dynamic Prototype Mask (DPM) based on two
self-evident prior knowledge. Specifically, we first devise a Hierarchical Mask
Generator which utilizes the hierarchical semantic to select the visible
pattern space between the high-quality holistic prototype and the feature
representation of the occluded input image. Under this condition, the occluded
representation could be well aligned in a selected subspace spontaneously.
Then, to enrich the feature representation of the high-quality holistic
prototype and provide a more complete feature space, we introduce a Head Enrich
Module to encourage different heads to aggregate different patterns
representation in the whole image. Extensive experimental evaluations conducted
on occluded and holistic person re-identification benchmarks demonstrate the
superior performance of the DPM over the state-of-the-art methods. The code is
released at https://github.com/stone96123/DPM.Comment: Accepted by ACM MM 202
Dynamic Feature Pruning and Consolidation for Occluded Person Re-Identification
Occluded person re-identification (ReID) is a challenging problem due to
contamination from occluders, and existing approaches address the issue with
prior knowledge cues, eg human body key points, semantic segmentations and etc,
which easily fails in the presents of heavy occlusion and other humans as
occluders. In this paper, we propose a feature pruning and consolidation (FPC)
framework to circumvent explicit human structure parse, which mainly consists
of a sparse encoder, a global and local feature ranking module, and a feature
consolidation decoder. Specifically, the sparse encoder drops less important
image tokens (mostly related to background noise and occluders) solely
according to correlation within the class token attention instead of relying on
prior human shape information. Subsequently, the ranking stage relies on the
preserved tokens produced by the sparse encoder to identify k-nearest neighbors
from a pre-trained gallery memory by measuring the image and patch-level
combined similarity. Finally, we use the feature consolidation module to
compensate pruned features using identified neighbors for recovering essential
information while disregarding disturbance from noise and occlusion.
Experimental results demonstrate the effectiveness of our proposed framework on
occluded, partial and holistic Re-ID datasets. In particular, our method
outperforms state-of-the-art results by at least 8.6% mAP and 6.0% Rank-1
accuracy on the challenging Occluded-Duke dataset.Comment: 12 pages, 9 figure
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