UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification (2012.04268v2)

Abstract: The main difficulty of person re-identification (ReID) lies in collecting annotated data and transferring the model across different domains. This paper presents UnrealPerson, a novel pipeline that makes full use of unreal image data to decrease the costs in both the training and deployment stages. Its fundamental part is a system that can generate synthesized images of high-quality and from controllable distributions. Instance-level annotation goes with the synthesized data and is almost free. We point out some details in image synthesis that largely impact the data quality. With 3,000 IDs and 120,000 instances, our method achieves a 38.5% rank-1 accuracy when being directly transferred to MSMT17. It almost doubles the former record using synthesized data and even surpasses previous direct transfer records using real data. This offers a good basis for unsupervised domain adaption, where our pre-trained model is easily plugged into the state-of-the-art algorithms towards higher accuracy. In addition, the data distribution can be flexibly adjusted to fit some corner ReID scenarios, which widens the application of our pipeline. We will publish our data synthesis toolkit and synthesized data in https://github.com/FlyHighest/UnrealPerson.

• The paper presents an innovative pipeline that uses synthesized images to eliminate the high costs of manual data annotation in person re-identification.
• It reports a notable achievement of 38.5% rank-1 accuracy on the MSMT17 dataset, outperforming previous methods relying on both real and synthetic data.
• The adaptive system utilizes virtual environments created with Unreal Engine to enhance domain generalization and support scalable re-identification applications.

UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification

The paper "UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification" addresses a significant challenge in the domain of person re-identification (ReID), which is the cost-intensive process of collecting and annotating large-scale datasets. The authors propose a novel pipeline that leverages synthesized images to alleviate the need for expensive, manually annotated data while enhancing the model's adaptability to various domains.

Overview of UnrealPerson

The primary contribution of this research is the UnrealPerson pipeline, designed to exploit virtual environments and generate high-quality synthesized data. This approach not only mitigates the need for manual data annotation but also enhances the model's ability to generalize across diverse scenarios. The synthesized data comes with free and accurate instance-level annotations. The paper reports a significant improvement in rank-1 accuracy, achieving 38.5% when directly transferred to the MSMT17 dataset, which is notable as it surpasses previous efforts that relied on both real and synthesized data.

Key Components and Methodology

1. Data Synthesis System: The core component of the UnrealPerson pipeline is its ability to create diverse and realistic virtual environments using Unreal Engine 4. By manipulating environmental parameters such as lighting and pedestrian appearances, the system generates a rich dataset with 3,000 identities and 120,000 instances.
2. Generalization and Domain Adaptation: The synthesized data's diversity reduces the model's susceptibility to overfitting on domain-specific features. The pipeline supports both supervised and unsupervised domain adaptation processes. For instance, it can be integrated with state-of-the-art algorithms to further enhance accuracy in unsupervised settings.
3. Scalability and Specialization: The UnrealPerson pipeline allows for the flexible adjustment of data distributions, catering to specific corner ReID scenarios such as low-illumination environments. This scalability and adaptability make the pipeline suitable for a broader range of applications.

Numerical Results and Comparisons

In terms of performance, the UnrealPerson pipeline exhibits strong numerical results. The rank-1 accuracy of 38.5% on MSMT17 represents nearly double the performance of previous methods using synthesized data and outperforms those using real-world data. This indicates the effectiveness of the UnrealPerson approach in overcoming traditional challenges related to data collection and deployment across different domains.

Implications and Speculative Future Developments

The implications of this research extend beyond immediate performance improvements. By reducing reliance on manual annotation, the UnrealPerson pipeline can significantly lower the entry barrier for deploying ReID technologies in real-world applications. Moreover, the approach opens avenues for exploring autonomous domain adaptation and real-time ReID solutions, potentially accelerating advancements in surveillance, security, and related fields.

Looking forward, the development of more sophisticated synthesis models and further integration with unsupervised learning techniques could enhance the pipeline's robustness and applicability. The exploration of self-supervised methodologies combined with UnrealPerson’s data synthesis could further advance the ReID field.

This paper contributes valuable insights and methodologies that could reshape the landscape of person re-identification, emphasizing the importance of synthetic data utilization and flexible adaptation strategies in AI research.