Abstract
Multi-object tracking (MOT) is a critical and challenging task in computer vision, particularly in situations involving objects with similar appearances but diverse movements, as seen in team sports. Current methods, largely reliant on object detection and appearance, often fail to track targets in such complex scenarios accurately. This limitation is further exacerbated by the lack of comprehensive and diverse datasets covering the full view of sports pitches. Addressing these issues, we introduce TeamTrack, a pioneering benchmark dataset specifically designed for MOT in sports. TeamTrack is an extensive collection of full-pitch video data from various sports, including soccer, basketball, and handball. Furthermore, we perform a comprehensive analysis and benchmarking effort to underscore TeamTrack's utility and potential impact. Our work signifies a crucial step forward, promising to elevate the precision and effectiveness of MOT in complex, dynamic settings such as team sports. The dataset, project code and competition is released at: this https URL.
Abstract (translated)
多目标跟踪（MOT）是计算机视觉领域一个关键而具有挑战性的任务，特别是在涉及具有相似外观但不同运动情况的情况，如团队运动中。当前的方法，主要依赖物体检测和外观，往往无法准确跟踪在这样的复杂场景中的目标。这个限制进一步加剧了缺乏全面和多样数据集覆盖体育场的不足。为了解决这些问题，我们介绍了专门为体育MOT设计的团队跟踪基准数据集TeamTrack。TeamTrack是一个包括各种体育项目完整场地视频数据的广泛收集。此外，我们进行了全面分析和基准测试，以强调TeamTrack的实用性和潜在影响。我们的工作标志着一个关键的进步，有望提高在复杂、动态的体育场景中MOT的精度和效果。该数据集、项目代码和比赛已发布在：https:// this URL。
URL
https://arxiv.org/abs/2404.13868
