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Real-time 3D Single Object Tracking with Transformer (2209.00860v1)

Published 2 Sep 2022 in cs.CV

Abstract: LiDAR-based 3D single object tracking is a challenging issue in robotics and autonomous driving. Currently, existing approaches usually suffer from the problem that objects at long distance often have very sparse or partially-occluded point clouds, which makes the features extracted by the model ambiguous. Ambiguous features will make it hard to locate the target object and finally lead to bad tracking results. To solve this problem, we utilize the powerful Transformer architecture and propose a Point-Track-Transformer (PTT) module for point cloud-based 3D single object tracking task. Specifically, PTT module generates fine-tuned attention features by computing attention weights, which guides the tracker focusing on the important features of the target and improves the tracking ability in complex scenarios. To evaluate our PTT module, we embed PTT into the dominant method and construct a novel 3D SOT tracker named PTT-Net. In PTT-Net, we embed PTT into the voting stage and proposal generation stage, respectively. PTT module in the voting stage could model the interactions among point patches, which learns context-dependent features. Meanwhile, PTT module in the proposal generation stage could capture the contextual information between object and background. We evaluate our PTT-Net on KITTI and NuScenes datasets. Experimental results demonstrate the effectiveness of PTT module and the superiority of PTT-Net, which surpasses the baseline by a noticeable margin, ~10% in the Car category. Meanwhile, our method also has a significant performance improvement in sparse scenarios. In general, the combination of transformer and tracking pipeline enables our PTT-Net to achieve state-of-the-art performance on both two datasets. Additionally, PTT-Net could run in real-time at 40FPS on NVIDIA 1080Ti GPU. Our code is open-sourced for the research community at https://github.com/shanjiayao/PTT.

Citations (40)

Summary

  • The paper introduces the novel PTT module, which leverages self-attention to refine feature representation amid LiDAR data challenges.
  • PTT-Net integrates the transformer-based module into key tracking stages, yielding around a 10% improvement in success and precision metrics.
  • Its real-time performance at 40 FPS on standard datasets underlines potential applications in autonomous navigation and dynamic robotics.

Real-time 3D Single Object Tracking with Transformer: An Overview

The paper "Real-time 3D Single Object Tracking with Transformer" presents an innovative methodological advancement in the domain of 3D object tracking using LiDAR data. In the context of autonomous driving and robotics, accurately tracking objects in three-dimensional space is paramount. This paper addresses the intrinsic challenges posed by the sparsity and partial occlusion of LiDAR point clouds, which typically lead to ambiguous feature extraction and subsequently, suboptimal tracking outcomes.

Main Contributions

The key contribution of this research is the development of a Point-Track-Transformer (PTT) module, which leverages the self-attention mechanism inherent in Transformer architectures to enhance feature representation. The PTT module is designed to mitigate the feature ambiguity by computing attention weights that prioritize significant features of the target object amidst clutter and noise inherent in real-world environments. This computational framework has been integrated into a novel tracking system named PTT-Net, built upon the dominant P2B architecture's foundation.

Technical Approach

PTT-Net incorporates the PTT module at pivotal stages, namely in the seeds voting stage and proposal generation phase, thereby exploiting contextual interrelations among point patches and between the target and its surrounding background. The transformer-based architecture uses feature embedding, position encoding, and self-attention blocks, enabling the model to proficiently capture spatial dependencies and refine attention-based feature maps. Consequently, PTT-Net achieves improved tracking accuracy by focusing on robust key-points and alleviating the influence of noise.

Experimental Validation

The efficacy of the proposed approach has been rigorously evaluated on the KITTI and NuScenes datasets. Experimental outcomes reveal a notable improvement over baseline methods, with approximately a 10% enhancement in success and precision metrics for the Car category. Moreover, PTT-Net sustains its performance even in sparsely featured scenarios, thereby underscoring the module's robustness. It achieves state-of-the-art performance while operating at a real-time speed of 40 FPS on an NVIDIA 1080Ti GPU, demonstrating both effectiveness and efficiency.

Implications for Future Research

The findings from this paper indicate a significant shift towards incorporating advanced neural architectures like transformers in perception tasks traditionally dominated by convolutional networks. The paper opens up further avenues for research, especially in extending the capabilities of transformer-based modules to more diverse classes of objects and environments. Additionally, the modular nature of the PTT could facilitate integration into other LiDAR-based applications, thus bridging the gap between 2D image processing and 3D point cloud analyses.

In future developments, researchers can explore augmenting the PTT framework with hybrid sensor inputs or multimodal data, potentially paving the way for even more robust object tracking systems in highly dynamic environments. Moreover, adapting this framework in the broader spectrum of autonomous navigation and dynamic scene understanding represents a promising direction.

In conclusion, the proposed PTT module and PTT-Net present a significant methodological contribution to the field of 3D object tracking. The successful application of transformer-based architectures to point cloud data illustrates the transformative potential of self-attention mechanisms in overcoming historical tracking challenges.