构建真实复杂场景下多人行踪追踪的大型基准数据集。
CrowdTrack: A Benchmark for Difficult Multiple Pedestrian Tracking in Real Scenarios
- 基于第一视角拍摄的真实复杂场景视频，覆盖多人遮挡与部分可见情况。
- 包含33段视频、5185条轨迹，每条轨迹有完整边界框和唯一ID标注。
- 适合研究复杂环境下鲁棒行人追踪算法，尤其对遮挡与外观变化敏感的模型。
多目标跟踪是计算机视觉的经典领域，其中行人跟踪应用价值高且研究最广泛。现有方法主要依赖运动或外观信息，但在复杂场景中常失效：运动信息受物体间相互遮挡影响，难以更新状态；外观信息因目标部分可见或图像模糊而不可靠。尽管从标注数据中学习应对这些情况是直接方案，但现有多目标跟踪数据集存在场景简单、非真实等问题。虽少数视频序列避免了这些问题，但数量不足以支撑研究。为此，我们提出一个大规模、困难的多人行踪追踪数据集——CrowdTrack，主要采用第一人称视角拍摄，所有视频均来自真实复杂场景。数据集包含33个视频，共5185条轨迹，每条轨迹均有完整边界框和唯一对象ID。该数据集将为复杂环境下仍保持有效性的算法研发提供平台。我们对数据集进行了全面分析，并在该数据集上测试了多个前沿模型及基础模型性能。数据集与代码已开源：https://github.com/loseevaya/CrowdTrack。
原文摘要 · Abstract (English)
Multi-object tracking is a classic field in computer vision. Among them, pedestrian tracking has extremely high application value and has become the most popular research category. Existing methods mainly use motion or appearance information for tracking, which is often difficult in complex scenarios. For the motion information, mutual occlusions between objects often prevent updating of the motion state; for the appearance information, non-robust results are often obtained due to reasons such as only partial visibility of the object or blurred images. Although learning how to perform tracking in these situations from the annotated data is the simplest solution, the existing MOT dataset fails to satisfy this solution. Existing methods mainly have two drawbacks: relatively simple scene composition and non-realistic scenarios. Although some of the video sequences in existing dataset do not have the above-mentioned drawbacks, the number is far from adequate for research purposes. To this end, we propose a difficult large-scale dataset for multi-pedestrian tracking, shot mainly from the first-person view and all from real-life complex scenarios. We name it ``CrowdTrack'' because there are numerous objects in most of the sequences. Our dataset consists of 33 videos, containing a total of 5,185 trajectories. Each object is annotated with a complete bounding box and a unique object ID. The dataset will provide a platform to facilitate the development of algorithms that remain effective in complex situations. We analyzed the dataset comprehensively and tested multiple SOTA models on our dataset. Besides, we analyzed the performance of the foundation models on our dataset. The dataset and project code is released at: https://github.com/loseevaya/CrowdTrack .
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