A flexible, effective and fast cross-view gait recognition network
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Updated
Jun 5, 2024 - Python
A flexible, effective and fast cross-view gait recognition network
This is the code for the paper "Gait Recognition in the Wild with Dense 3D Representations and A Benchmark. (CVPR 2022)", "Gait Recognition in the Wild with Multi-hop Temporal Switch", and "Parsing is All You Need for Accurate Gait Recognition in the Wild".
Official repository for "GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition" (ICIP'21)
Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits
An End-to-end Network for Gait Based Human Identification
Multimodal Dataset of Freezing of Gait in Parkinson's Disease
This is the code for the paper "Jinkai Zheng, Xinchen Liu, Chenggang Yan, Jiyong Zhang, Wu Liu, Xiaoping Zhang and Tao Mei: TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition. ISCAS 2021" (Best Paper Award - Honorable Mention)
Gait recognition system based on deep learning models.
Official repository for "GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer"
GaitFormer Official Codebase for the paper "Learning Gait Representations with Noisy Multi-Task Learning"
Raw dataset from "Signal Processing and Machine Learning for Diplegia Classification" and "Gait-Based Diplegia Classification Using LSMT Networks"
An Effective Pretreatment Strategy for Gait Recognition
Source code for the Gait Recognition using LSTM, presented in the paper "Multi-model Long Short-term Memory Network for Gait Recognition using Window-based Data Segment"
The benchmark experiments of paper "ReSGait: The real scene gait dataset".
Gait recognition system based on YOLOv8
Official codebase for "Exploring Self-Supervised Vision Transformers for Gait Recognition in the Wild"
Official Implementation of Open-Set Biometrics: Beyond Good Closed-Set Models (ECCV 2024)
This project leverages state-of-the-art deep learning models—VGG-16, InceptionV1, ViT, EfficientNet-B0, and ResNet50—for gait image classification. By applying decision level fusion, feature level fusion, and hybrid fusion techniques, the project achieves enhanced accuracy, making it suitable for any image classification task.
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