The goal of multi-object tracking is to detect and track all objects in a
scene while maintaining unique identifiers for each, by associating their
bounding boxes across video frames. This association relies on matching motion
and appearance patterns of detected objects. This task is especially hard in
case of scenarios involving dynamic and non-linear motion patterns. In this
paper, we introduce DeepMoveSORT, a novel, carefully engineered multi-object
tracker designed specifically for such scenarios. In addition to standard
methods of appearance-based association, we improve motion-based association by
employing deep learnable filters (instead of the most commonly used Kalman
filter) and a rich set of newly proposed heuristics. Our improvements to
motion-based association methods are severalfold. First, we propose a new
transformer-based filter architecture, TransFilter, which uses an object's
motion history for both motion prediction and noise filtering. We further
enhance the filter's performance by careful handling of its motion history and
accounting for camera motion. Second, we propose a set of heuristics that
exploit cues from the position, shape, and confidence of detected bounding
boxes to improve association performance. Our experimental evaluation
demonstrates that DeepMoveSORT outperforms existing trackers in scenarios
featuring non-linear motion, surpassing state-of-the-art results on three such
datasets. We also perform a thorough ablation study to evaluate the
contributions of different tracker components which we proposed. Based on our
study, we conclude that using a learnable filter instead of the Kalman filter,
along with appearance-based association is key to achieving strong general
tracking performance.

我们介绍了 DeepMoveSORT，这是一个专门设计用于动态和非线性运动模式场景的多目标跟踪器。通过使用可学习的深度滤波器和一系列新提出的启发式方法，我们改善了基于运动的关联方法，从而提高了关联性能，并在三个具有非线性运动的数据集上超过了现有跟踪器的最新结果。我们的研究表明，使用可学习的滤波器代替 Kalman 滤波器，并结合基于外观的关联是实现强大的通用跟踪性能的关键。

非线性运动的高效目标追踪器工程化

Engineering an Efficient Object Tracker for Non-Linear Motion

Multi-object tracking in sports scenes plays a critical role in gathering
players statistics, supporting further analysis, such as automatic tactical
analysis. Yet existing MOT benchmarks cast little attention on the domain,
limiting its development. In this work, we present a new large-scale
multi-object tracking dataset in diverse sports scenes, coined as
\emph{SportsMOT}, where all players on the court are supposed to be tracked. It
consists of 240 video sequences, over 150K frames (almost 15\times MOT17) and
over 1.6M bounding boxes (3\times MOT17) collected from 3 sports categories,
including basketball, volleyball and football. Our dataset is characterized
with two key properties: 1) fast and variable-speed motion and 2) similar yet
distinguishable appearance. We expect SportsMOT to encourage the MOT trackers
to promote in both motion-based association and appearance-based association.
We benchmark several state-of-the-art trackers and reveal the key challenge of
SportsMOT lies in object association. To alleviate the issue, we further
propose a new multi-object tracking framework, termed as \emph{MixSort},
introducing a MixFormer-like structure as an auxiliary association model to
prevailing tracking-by-detection trackers. By integrating the customized
appearance-based association with the original motion-based association,
MixSort achieves state-of-the-art performance on SportsMOT and MOT17. Based on
MixSort, we give an in-depth analysis and provide some profound insights into
SportsMOT. The dataset and code will be available at
this https URL

本文提出了一种新的大规模多目标追踪数据集 SportsMOT，旨在促进运动跟踪器在运动和外观等方面的进步，通过比较当下几种状态最好的跟踪器和 MixSort 框架，我们发现 SportsMOT 的关键挑战在于对象关联，MixSort 框架通过将自定义的基于外观的关联与原始的基于运动的关联相结合，实现了在 SportsMOT 和 MOT17 上的最先进的性能表现。