2,597 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
Illumination Distillation Framework for Nighttime Person Re-Identification and A New Benchmark
Nighttime person Re-ID (person re-identification in the nighttime) is a very
important and challenging task for visual surveillance but it has not been
thoroughly investigated. Under the low illumination condition, the performance
of person Re-ID methods usually sharply deteriorates. To address the low
illumination challenge in nighttime person Re-ID, this paper proposes an
Illumination Distillation Framework (IDF), which utilizes illumination
enhancement and illumination distillation schemes to promote the learning of
Re-ID models. Specifically, IDF consists of a master branch, an illumination
enhancement branch, and an illumination distillation module. The master branch
is used to extract the features from a nighttime image. The illumination
enhancement branch first estimates an enhanced image from the nighttime image
using a nonlinear curve mapping method and then extracts the enhanced features.
However, nighttime and enhanced features usually contain data noise due to
unstable lighting conditions and enhancement failures. To fully exploit the
complementary benefits of nighttime and enhanced features while suppressing
data noise, we propose an illumination distillation module. In particular, the
illumination distillation module fuses the features from two branches through a
bottleneck fusion model and then uses the fused features to guide the learning
of both branches in a distillation manner. In addition, we build a real-world
nighttime person Re-ID dataset, named Night600, which contains 600 identities
captured from different viewpoints and nighttime illumination conditions under
complex outdoor environments. Experimental results demonstrate that our IDF can
achieve state-of-the-art performance on two nighttime person Re-ID datasets
(i.e., Night600 and Knight ). We will release our code and dataset at
https://github.com/Alexadlu/IDF.Comment: Accepted by TM
Lightweight Learning for Partial and Occluded Person Re-identification
Occluded and partial person re-identification (re-ID) problems have emerged as challenging research topics in the area of computer vision. Existing part-based models, with complex designs, fail to properly tackle these problems. The reasons for their failures are two-fold. Firstly, individual body part appearances are not discriminative enough to distinguish between two closely appearing persons. Secondly, re-identification datasets typically lack detailed human body-part annotations. To address these challenges, we present a lightweight yet accurate solution for partial person re-identification. Our proposed approach consists of two key components, namely, design of a lightweight Unary-Binary projective Dictionary Learning (UBDL) model, and, construction of a similarity matrix for distilling knowledge from the deep Omni-scale network (OSNet) to UBDL. The unary dictionary (UD) pair encodes patches horizontally, ignoring the viewpoints. The binary dictionary (BD) pairs, on the other hand, are learned between two views, giving more weight to less occluded vertical patches for improving the correspondence across the views. We formulate appropriate convex objective functions for unary and binary cases by incorporating the above knowledge similarity matrix. Closed-form solutions are obtained for updating unary and binary dictionary components. Final matching scores are computed by fusing unary and binary matching scores with adaptive weighting of relevant cross-view patches. Extensive experiments and ablation studies on a number of occluded and partial re-identification datasets like Occluded-REID (O-REID), Partial-REID (PREID) and Partial-iLIDS (P-iLIDS), clearly showcase the merits of our proposed solution.</p
Erasing, Transforming, and Noising Defense Network for Occluded Person Re-Identification
Occlusion perturbation presents a significant challenge in person
re-identification (re-ID), and existing methods that rely on external visual
cues require additional computational resources and only consider the issue of
missing information caused by occlusion. In this paper, we propose a simple yet
effective framework, termed Erasing, Transforming, and Noising Defense Network
(ETNDNet), which treats occlusion as a noise disturbance and solves occluded
person re-ID from the perspective of adversarial defense. In the proposed
ETNDNet, we introduce three strategies: Firstly, we randomly erase the feature
map to create an adversarial representation with incomplete information,
enabling adversarial learning of identity loss to protect the re-ID system from
the disturbance of missing information. Secondly, we introduce random
transformations to simulate the position misalignment caused by occlusion,
training the extractor and classifier adversarially to learn robust
representations immune to misaligned information. Thirdly, we perturb the
feature map with random values to address noisy information introduced by
obstacles and non-target pedestrians, and employ adversarial gaming in the
re-ID system to enhance its resistance to occlusion noise. Without bells and
whistles, ETNDNet has three key highlights: (i) it does not require any
external modules with parameters, (ii) it effectively handles various issues
caused by occlusion from obstacles and non-target pedestrians, and (iii) it
designs the first GAN-based adversarial defense paradigm for occluded person
re-ID. Extensive experiments on five public datasets fully demonstrate the
effectiveness, superiority, and practicality of the proposed ETNDNet. The code
will be released at \url{https://github.com/nengdong96/ETNDNet}
Weakly-supervised Part-Attention and Mentored Networks for Vehicle Re-Identification
Vehicle re-identification (Re-ID) aims to retrieve images with the same
vehicle ID across different cameras. Current part-level feature learning
methods typically detect vehicle parts via uniform division, outside tools, or
attention modeling. However, such part features often require expensive
additional annotations and cause sub-optimal performance in case of unreliable
part mask predictions. In this paper, we propose a weakly-supervised
Part-Attention Network (PANet) and Part-Mentored Network (PMNet) for Vehicle
Re-ID. Firstly, PANet localizes vehicle parts via part-relevant channel
recalibration and cluster-based mask generation without vehicle part
supervisory information. Secondly, PMNet leverages teacher-student guided
learning to distill vehicle part-specific features from PANet and performs
multi-scale global-part feature extraction. During inference, PMNet can
adaptively extract discriminative part features without part localization by
PANet, preventing unstable part mask predictions. We address this Re-ID issue
as a multi-task problem and adopt Homoscedastic Uncertainty to learn the
optimal weighing of ID losses. Experiments are conducted on two public
benchmarks, showing that our approach outperforms recent methods, which require
no extra annotations by an average increase of 3.0% in CMC@5 on VehicleID and
over 1.4% in mAP on VeRi776. Moreover, our method can extend to the occluded
vehicle Re-ID task and exhibits good generalization ability.Comment: This work has been submitted to the IEEE for possible publication.
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