311 research outputs found
Parsing is All You Need for Accurate Gait Recognition in the Wild
Binary silhouettes and keypoint-based skeletons have dominated human gait
recognition studies for decades since they are easy to extract from video
frames. Despite their success in gait recognition for in-the-lab environments,
they usually fail in real-world scenarios due to their low information entropy
for gait representations. To achieve accurate gait recognition in the wild,
this paper presents a novel gait representation, named Gait Parsing Sequence
(GPS). GPSs are sequences of fine-grained human segmentation, i.e., human
parsing, extracted from video frames, so they have much higher information
entropy to encode the shapes and dynamics of fine-grained human parts during
walking. Moreover, to effectively explore the capability of the GPS
representation, we propose a novel human parsing-based gait recognition
framework, named ParsingGait. ParsingGait contains a Convolutional Neural
Network (CNN)-based backbone and two light-weighted heads. The first head
extracts global semantic features from GPSs, while the other one learns mutual
information of part-level features through Graph Convolutional Networks to
model the detailed dynamics of human walking. Furthermore, due to the lack of
suitable datasets, we build the first parsing-based dataset for gait
recognition in the wild, named Gait3D-Parsing, by extending the large-scale and
challenging Gait3D dataset. Based on Gait3D-Parsing, we comprehensively
evaluate our method and existing gait recognition methods. The experimental
results show a significant improvement in accuracy brought by the GPS
representation and the superiority of ParsingGait. The code and dataset are
available at https://gait3d.github.io/gait3d-parsing-hp .Comment: 16 pages, 14 figures, ACM MM 2023 accepted, project page:
https://gait3d.github.io/gait3d-parsing-h
GaitFM: Fine-grained Motion Representation for Gait Recognition
Gait recognition aims at identifying individual-specific walking patterns,
which is highly dependent on the observation of the different periodic
movements of each body part. However, most existing methods treat each part
equally and neglect the data redundancy due to the high sampling rate of gait
sequences. In this work, we propose a fine-grained motion representation
network (GaitFM) to improve gait recognition performance in three aspects.
First, a fine-grained part sequence learning (FPSL) module is designed to
explore part-independent spatio-temporal representations. Secondly, a
frame-wise compression strategy, called local motion aggregation (LMA), is used
to enhance motion variations. Finally, a weighted generalized mean pooling
(WGeM) layer works to adaptively keep more discriminative information in the
spatial downsampling. Experiments on two public datasets, CASIA-B and OUMVLP,
show that our approach reaches state-of-the-art performances. On the CASIA-B
dataset, our method achieves rank-1 accuracies of 98.0%, 95.7% and 87.9% for
normal walking, walking with a bag and walking with a coat, respectively. On
the OUMVLP dataset, our method achieved a rank-1 accuracy of 90.5%
GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition
As a unique biometric feature that can be recognized at a distance, gait has
broad applications in crime prevention, forensic identification and social
security. To portray a gait, existing gait recognition methods utilize either a
gait template, where temporal information is hard to preserve, or a gait
sequence, which must keep unnecessary sequential constraints and thus loses the
flexibility of gait recognition. In this paper we present a novel perspective,
where a gait is regarded as a set consisting of independent frames. We propose
a new network named GaitSet to learn identity information from the set. Based
on the set perspective, our method is immune to permutation of frames, and can
naturally integrate frames from different videos which have been filmed under
different scenarios, such as diverse viewing angles, different clothes/carrying
conditions. Experiments show that under normal walking conditions, our
single-model method achieves an average rank-1 accuracy of 95.0% on the CASIA-B
gait dataset and an 87.1% accuracy on the OU-MVLP gait dataset. These results
represent new state-of-the-art recognition accuracy. On various complex
scenarios, our model exhibits a significant level of robustness. It achieves
accuracies of 87.2% and 70.4% on CASIA-B under bag-carrying and coat-wearing
walking conditions, respectively. These outperform the existing best methods by
a large margin. The method presented can also achieve a satisfactory accuracy
with a small number of frames in a test sample, e.g., 82.5% on CASIA-B with
only 7 frames. The source code has been released at
https://github.com/AbnerHqC/GaitSet.Comment: AAAI 2019, code is available at https://github.com/AbnerHqC/GaitSe
LiCamGait: Gait Recognition in the Wild by Using LiDAR and Camera Multi-modal Visual Sensors
LiDAR can capture accurate depth information in large-scale scenarios without
the effect of light conditions, and the captured point cloud contains
gait-related 3D geometric properties and dynamic motion characteristics. We
make the first attempt to leverage LiDAR to remedy the limitation of
view-dependent and light-sensitive camera for more robust and accurate gait
recognition. In this paper, we propose a LiDAR-camera-based gait recognition
method with an effective multi-modal feature fusion strategy, which fully
exploits advantages of both point clouds and images. In particular, we propose
a new in-the-wild gait dataset, LiCamGait, involving multi-modal visual data
and diverse 2D/3D representations. Our method achieves state-of-the-art
performance on the new dataset. Code and dataset will be released when this
paper is published
GaitRef: Gait Recognition with Refined Sequential Skeletons
Identifying humans with their walking sequences, known as gait recognition,
is a useful biometric understanding task as it can be observed from a long
distance and does not require cooperation from the subject. Two common
modalities used for representing the walking sequence of a person are
silhouettes and joint skeletons. Silhouette sequences, which record the
boundary of the walking person in each frame, may suffer from the variant
appearances from carried-on objects and clothes of the person. Framewise joint
detections are noisy and introduce some jitters that are not consistent with
sequential detections. In this paper, we combine the silhouettes and skeletons
and refine the framewise joint predictions for gait recognition. With temporal
information from the silhouette sequences. We show that the refined skeletons
can improve gait recognition performance without extra annotations. We compare
our methods on four public datasets, CASIA-B, OUMVLP, Gait3D and GREW, and show
state-of-the-art performance.Comment: IJCB 2023. Code is available at
https://github.com/haidongz-usc/GaitRe
Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark
Gait depicts individuals' unique and distinguishing walking patterns and has
become one of the most promising biometric features for human identification.
As a fine-grained recognition task, gait recognition is easily affected by many
factors and usually requires a large amount of completely annotated data that
is costly and insatiable. This paper proposes a large-scale self-supervised
benchmark for gait recognition with contrastive learning, aiming to learn the
general gait representation from massive unlabelled walking videos for
practical applications via offering informative walking priors and diverse
real-world variations. Specifically, we collect a large-scale unlabelled gait
dataset GaitLU-1M consisting of 1.02M walking sequences and propose a
conceptually simple yet empirically powerful baseline model GaitSSB.
Experimentally, we evaluate the pre-trained model on four widely-used gait
benchmarks, CASIA-B, OU-MVLP, GREW and Gait3D with or without transfer
learning. The unsupervised results are comparable to or even better than the
early model-based and GEI-based methods. After transfer learning, our method
outperforms existing methods by a large margin in most cases. Theoretically, we
discuss the critical issues for gait-specific contrastive framework and present
some insights for further study. As far as we know, GaitLU-1M is the first
large-scale unlabelled gait dataset, and GaitSSB is the first method that
achieves remarkable unsupervised results on the aforementioned benchmarks. The
source code of GaitSSB will be integrated into OpenGait which is available at
https://github.com/ShiqiYu/OpenGait
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