This paper explores a pragmatic approach to multiple object tracking where
the main focus is to associate objects efficiently for online and realtime
applications. To this end, detection quality is identified as a key factor
influencing tracking performance, where changing the detector can improve
tracking by up to 18.9%. Despite only using a rudimentary combination of
familiar techniques such as the Kalman Filter and Hungarian algorithm for the
tracking components, this approach achieves an accuracy comparable to
state-of-the-art online trackers. Furthermore, due to the simplicity of our
tracking method, the tracker updates at a rate of 260 Hz which is over 20x
faster than other state-of-the-art trackers.

本文探索一种实用的多目标跟踪方法，强调高效地为实时应用程序分配对象。在追踪组件中，使用了卡尔曼滤波器和匈牙利算法等熟悉技术的简单组合，同时也发现检测质量是影响跟踪性能的关键因素之一。通过更改检测器，跟踪精度可以提高高达 18.9％。此外，由于跟踪方法的简单性，该跟踪器的更新速率为 260 Hz，比其他最先进的跟踪器快 20 倍以上。

简单在线实时跟踪

Simple Online and Realtime Tracking

Paper-by-paper results make it easy to miss the forest for the trees.We
analyse the remarkable progress of the last decade by discussing the main ideas
explored in the 40+ detectors currently present in the Caltech pedestrian
detection benchmark. We observe that there exist three families of approaches,
all currently reaching similar detection quality. Based on our analysis, we
study the complementarity of the most promising ideas by combining multiple
published strategies. This new decision forest detector achieves the current
best known performance on the challenging Caltech-USA dataset.

分析行人检测领域中 40 多个探测器中探索的主要思想，发现存在 3 类方法，目前都达到了相似的检测质量，结合多种发布的策略，建立了一个新的决策树检测器，在 Caltech-USA 数据集上实现了当前已知的最佳性能。