16,974 research outputs found
Occluded Person Re-identification
Person re-identification (re-id) suffers from a serious occlusion problem
when applied to crowded public places. In this paper, we propose to retrieve a
full-body person image by using a person image with occlusions. This differs
significantly from the conventional person re-id problem where it is assumed
that person images are detected without any occlusion. We thus call this new
problem the occluded person re-identitification. To address this new problem,
we propose a novel Attention Framework of Person Body (AFPB) based on deep
learning, consisting of 1) an Occlusion Simulator (OS) which automatically
generates artificial occlusions for full-body person images, and 2) multi-task
losses that force the neural network not only to discriminate a person's
identity but also to determine whether a sample is from the occluded data
distribution or the full-body data distribution. Experiments on a new occluded
person re-id dataset and three existing benchmarks modified to include
full-body person images and occluded person images show the superiority of the
proposed method.Comment: 6 pages, 7 figures, IEEE International Conference of Multimedia and
Expo 201
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
Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning
Occluded person re-identification (Re-ID) in images captured by multiple
cameras is challenging because the target person is occluded by pedestrians or
objects, especially in crowded scenes. In addition to the processes performed
during holistic person Re-ID, occluded person Re-ID involves the removal of
obstacles and the detection of partially visible body parts. Most existing
methods utilize the off-the-shelf pose or parsing networks as pseudo labels,
which are prone to error. To address these issues, we propose a novel Occlusion
Correction Network (OCNet) that corrects features through relational-weight
learning and obtains diverse and representative features without using external
networks. In addition, we present a simple concept of a center feature in order
to provide an intuitive solution to pedestrian occlusion scenarios.
Furthermore, we suggest the idea of Separation Loss (SL) for focusing on
different parts between global features and part features. We conduct extensive
experiments on five challenging benchmark datasets for occluded and holistic
Re-ID tasks to demonstrate that our method achieves superior performance to
state-of-the-art methods especially on occluded scene.Comment: ICASSP 202
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
Learning Disentangled Representation Implicitly via Transformer for Occluded Person Re-Identification
Person re-identification (re-ID) under various occlusions has been a
long-standing challenge as person images with different types of occlusions
often suffer from misalignment in image matching and ranking. Most existing
methods tackle this challenge by aligning spatial features of body parts
according to external semantic cues or feature similarities but this alignment
approach is complicated and sensitive to noises. We design DRL-Net, a
disentangled representation learning network that handles occluded re-ID
without requiring strict person image alignment or any additional supervision.
Leveraging transformer architectures, DRL-Net achieves alignment-free re-ID via
global reasoning of local features of occluded person images. It measures image
similarity by automatically disentangling the representation of undefined
semantic components, e.g., human body parts or obstacles, under the guidance of
semantic preference object queries in the transformer. In addition, we design a
decorrelation constraint in the transformer decoder and impose it over object
queries for better focus on different semantic components. To better eliminate
interference from occlusions, we design a contrast feature learning technique
(CFL) for better separation of occlusion features and discriminative ID
features. Extensive experiments over occluded and holistic re-ID benchmarks
(Occluded-DukeMTMC, Market1501 and DukeMTMC) show that the DRL-Net achieves
superior re-ID performance consistently and outperforms the state-of-the-art by
large margins for Occluded-DukeMTMC
- …