利用点云特征去相关实现类别无关跟踪
Towards Class-agnostic Tracking Using Feature Decorrelation in Point Clouds
AI总结：
针对点云单目标跟踪难以泛化到未见类别的问题，提出模板与搜索区域融合特征去相关方法，在KITTI和NuScenes上相较P2B、BAT显著提升类别无关跟踪性能。
摘要：
由于LiDAR传感器在3D视觉中的普及，点云中的单目标跟踪正受到越来越多的关注。然而，现有基于深度神经网络的方法主要侧重于针对不同类别训练不同模型，这使得它们在实际应用中遇到训练阶段未见过的类别时无法取得良好表现。本工作研究LiDAR点云中一项更具挑战性的任务——类别无关跟踪，即应学习一个通用模型，以适用于已见和未见类别中任意指定目标。具体而言，我们首先在测试时向最先进跟踪器暴露未见类别，考察其类别无关性能，发现类别无关跟踪的一个关键因素在于：当分布从已见类别转移到未见类别时，如何约束模板与搜索区域之间的融合特征以保持泛化能力。因此，我们提出一种特征去相关方法来解决该问题，该方法通过一组学习到的权重消除融合特征中的虚假相关，并进一步使搜索区域在前景点之间保持一致，同时使前景与背景点之间具有区分性。在KITTI和NuScenes上的实验表明，与先进跟踪器P2B和BAT进行基准比较时，所提方法能够取得可观提升，尤其是在跟踪未见目标时。
英文摘要：
Single object tracking in point clouds has been attracting more and more attention owing to the presence of LiDAR sensors in 3D vision. However, the existing methods based on deep neural networks focus mainly on training different models for different categories, which makes them unable to perform well in real-world applications when encountering classes unseen during the training phase. In this work, we investigate a more challenging task in the LiDAR point clouds, class-agnostic tracking, where a general model is supposed to be learned for any specified targets of both observed and unseen categories. In particular, we first investigate the class-agnostic performances of the state-of-the-art trackers via exposing the unseen categories to them during testing, finding that a key factor for class-agnostic tracking is how to constrain fused features between the template and search region to maintain generalization when the distribution is shifted from observed to unseen classes. Therefore, we propose a feature decorrelation method to address this problem, which eliminates the spurious correlations of the fused features through a set of learned weights and further makes the search region consistent among foreground points and distinctive between foreground and background points. Experiments on the KITTI and NuScenes demonstrate that the proposed method can achieve considerable improvements by benchmarking against the advanced trackers P2B and BAT, especially when tracking unseen objects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
