Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification

Konsep Inti

This paper proposes the first cross-modality gait recognition framework, named CrossGait, that bridges the gap between LiDAR point clouds and camera silhouettes for accurate pedestrian identification across diverse sensors.

Abstrak

The paper focuses on the problem of cross-modality gait recognition, which aims to accurately identify pedestrians across diverse vision sensors like LiDAR and cameras. The key challenges are: 1) learning a modality-shared feature space to enable retrieval between distinct modalities, and 2) simultaneously maintaining modality-specific features for robust single-modality recognition.
To address these challenges, the authors propose the CrossGait framework:

It employs a two-stage approach - first learning modality-specific features, then aligning them into a unified modality-shared feature space.
The Prototypical Modality-Shared Attention Module (PMAM) extracts common features across modalities to facilitate cross-modality matching.
The Cross-modality Feature Adapter (CMFA) transforms modality-specific features into a shared feature space.
Extensive experiments on the SUSTech1K dataset demonstrate CrossGait's effectiveness in cross-modality gait recognition, outperforming existing cross-modality methods. It also maintains satisfactory performance in single-modality settings.
The authors further show CrossGait's generalization to handle different camera-based gait representations like silhouettes, parsing images, and skeleton maps.

Statistik

LiDAR-based gait recognition achieves 86.7% Rank-1 accuracy on the SUSTech1K dataset.
Camera-based gait recognition achieves 76.1% Rank-1 accuracy on the SUSTech1K dataset.

Kutipan

"To the best of our knowledge, this study represents the first exploration of cross-modality gait recognition involving both the LiDAR and visual camera."
"CrossGait demonstrates its ability to retrieve pedestrians across various modalities from different sensors in diverse scenes. Furthermore, it consistently delivers satisfactory performance even in single-modality settings."

Wawasan Utama Disaring Dari

Cross-Modality Gait Recognition

by Rui Wang,Chu... pada arxiv.org 04-08-2024

https://arxiv.org/pdf/2404.04120.pdf

Pertanyaan yang Lebih Dalam

How can the proposed CrossGait framework be extended to handle other modalities beyond LiDAR and camera, such as event images, infrared images, or WiFi signals

To extend the CrossGait framework to handle other modalities like event images, infrared images, or WiFi signals, several modifications and enhancements can be implemented.

Feature Extraction: Modify the feature encoders to accommodate the specific characteristics of the new modalities. For event images, consider using convolutional neural networks (CNNs) tailored for event-based data processing. For infrared images, adjust the feature encoders to capture thermal signatures effectively. For WiFi signals, explore signal processing techniques to extract relevant features.

Modality Integration: Develop new modules within CrossGait to integrate the features extracted from different modalities. This may involve creating specific attention mechanisms or fusion strategies to combine information from diverse sources effectively.

Training Data Augmentation: Generate synthetic data for the new modalities to enhance the model's ability to generalize across different types of input. This can help in improving the model's performance on unseen data during inference.

Evaluation and Fine-tuning: Conduct thorough evaluations on datasets containing the new modalities to fine-tune the model parameters and optimize its performance. This iterative process will help in identifying the best configurations for handling the additional modalities.

By incorporating these strategies, the CrossGait framework can be extended to handle a broader range of modalities, enabling more versatile and robust cross-modality gait recognition across various sensor inputs.

What are the potential limitations of the current cross-modality gait recognition approach, and how can they be addressed in future research

The current cross-modality gait recognition approach may face several limitations that could be addressed in future research:

Limited Dataset Diversity: The performance of the model may be hindered by a lack of diverse training data representing various real-world scenarios. Future research could focus on collecting and annotating datasets with a wider range of environmental conditions and gait variations.

Generalization to Unseen Modalities: The model's ability to generalize to entirely new modalities beyond the ones it was trained on could be limited. Future work could explore techniques for domain adaptation and transfer learning to improve generalization capabilities.

Robustness to Environmental Changes: Changes in lighting conditions, occlusions, or other environmental factors may impact the model's performance. Future research could investigate robust feature extraction methods that are less sensitive to such variations.

Privacy and Ethical Considerations: As gait recognition technology becomes more prevalent, addressing privacy concerns and ethical implications related to surveillance and data security is crucial. Future research should prioritize developing privacy-preserving techniques and ensuring ethical use of gait recognition systems.

By addressing these limitations, future research can enhance the effectiveness and applicability of cross-modality gait recognition systems in real-world settings.

Given the importance of clothing variations in real-world gait recognition, how can the CrossGait framework be further improved to handle more challenging clothing conditions

To improve the CrossGait framework's performance in handling challenging clothing conditions in gait recognition, several enhancements can be considered:

Clothing-Invariant Features: Develop feature extraction methods that focus on capturing gait patterns that are less affected by variations in clothing. This could involve learning clothing-invariant features through data augmentation techniques or specialized network architectures.

Dynamic Clothing Modeling: Incorporate dynamic modeling of clothing variations into the framework. This could involve creating adaptive models that can adjust to different clothing styles and textures during the recognition process.

Multi-Modal Fusion: Explore the fusion of multiple modalities, such as RGB images and depth maps, to enhance the model's ability to recognize individuals based on their gait patterns regardless of clothing variations. Multi-modal fusion can provide complementary information that improves recognition accuracy.

Adversarial Training: Implement adversarial training techniques to make the model more robust to variations in clothing appearance. Adversarial training can help the model learn to focus on intrinsic gait characteristics while disregarding irrelevant variations caused by clothing.

By incorporating these strategies, the CrossGait framework can be further improved to handle challenging clothing conditions in gait recognition, leading to more accurate and reliable identification of individuals across different scenarios.