霸榜SOTA！多目标跟踪算法ByteTrack！华科、港大、字节出品！

一时失言乱红尘 2022-09-17 00:58 689阅读 0赞

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转载于：Sophia知乎

https://zhuanlan.zhihu.com/p/423487567

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今天推荐的是华科、港大、字节出品的多目标跟踪算法ByteTrack！目前在多目标跟踪MOT榜单第一！30FPS速度在MOT17上成绩是80.3 MOTA，77.3 IDF1和63.1 HOTA。文章开源算法！2021年10月最新文章！

![bf9036bf4dcad4051b7a204fe10d7596.png][]

文章地址: http://arxiv.org/abs/2110.06864

开源地址: https://github.com/ifzhang/ByteTrack

Leaderboard: https://motchallenge.net/results/MOT17/?det=Private

## 摘要： ##

![446fdb0d66b328a36ff436462c3abc75.png][]

多目标跟踪(MOT)的目标是估计视频中目标的边界框和特征。大多数方法通过关联得分高于阈值的检测框来获得身份。低检测分数的物体，例如被遮挡的物体，被简单地丢弃，这导致不可忽略的真实物体丢失和碎片轨迹。为了解决这一问题，我们提出了一种简单、有效、通用的关联方法，即通过关联每个检测框而不是高分检测框进行跟踪。对于分数较低的检测框，我们利用它们与小轨迹的相似性来恢复真实目标，并过滤掉背景检测。当应用于9个不同的最先进的跟踪器时，我们的方法实现了从1到10点的IDF1分数的一致改进。为了提高MOT的性能，我们设计了一个简单而强大的跟踪器——ByteTrack。我们首次在单个V100 GPU上以30 FPS的运行速度，在mo17测试集上实现了80.3 MOTA, 77.3 IDF1和63.1 HOTA。

效果对比：

![8a5f627c33b9b0996db1e7130652db6e.png][]

目前大部分MOT方法会选择一个阈值，只保留高于这个阈值的检测结果来做关联得到跟踪结果，低于这个阈值的检测结果直接丢弃。BYTE将高分框和低分框分开处理，利用低分检测框和跟踪轨迹之间的相似性，从低分框中挖掘出真正的物体，过滤掉背景。

## 流程 ##

（1）分两类：高分框+低分框

（2）第一次先用高分框和之前的轨迹匹配

（3）第二次用低分框与第一次没有匹配上的高分框的跟踪轨迹进行匹配

（4）对于没有匹配上轨迹的高分框，建立一个新的跟踪轨迹。对于没有匹配检测框的跟踪轨迹，保留30帧，以便于后面再次匹配

![c28f1d4574a8002acb60fce5988df016.png][]

我们的方法的例子关联每个检测框。(a)显示所有检测框及其得分。(b)显示了以往方法获得的tracklet，这些tracklet与得分高于阈值(即0.5)的检测框相关联。相同的盒子颜色代表相同的身份。(c)显示用我们的方法得到的轨迹。虚线框表示使用卡尔曼滤波的前一个轨迹的预测框。两个低分检测框与之前的tracklet正确匹配

## 算法流程 ##

![fcd115b93d2c827f98d9b7ca6ede2696.png][] ![94a30edd6b53907b4d7f3f8fafb24ae1.png][]

使用YOLOX作为目标检测器。在数据关联的过程中，和SORT一样，只使用卡尔曼滤波来预测当前帧的跟踪轨迹在下一帧的位置，预测的框和实际的检测框之间的IoU作为两次匹配时的相似度，通过匈牙利算法完成匹配

## 实验效果： ##

![48f6ba0c9c68608400fdbe5ba3223e95.png][] ![b04a70967bd62db8c5a290d25fac2e46.png][] ![5e0eaff00e72f5b73966488c70128055.png][] ![c401d7cd76ed8c67e584166611dc2255.png][] ![ecfa0a55b7247df685aac2fd7e9911cf.png][] ![cc2adb31ca4e78c9fc2533f90122fdda.png][]

作者做的内容充分，并且很有意思，大家可以看一下代码进一步学习

**往期论文推荐：**

[跨界思维！Hinton新作！目标检测新算法Pix2Seq！效果显著！谷歌大脑出品！][Hinton_Pix2Seq]

[高效！Anchor DETR：旷视提出一种基于Transformer的目标检测神器！][Anchor DETR_Transformer]

[小目标神器！TPH-YOLOv5：将Transformer预测加载Yolov5！][TPH-YOLOv5_Transformer_Yolov5]

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[高效新思路！实例分割算法！腾讯、华科出品！入选ICCV 2021!][ICCV 2021]

[超强总结！456篇参考文献！《计算机视觉对抗攻击与防御》综述！][TPH-YOLOv5_Transformer_Yolov5]

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[优秀！一种巧妙组合算法YOLOX！旷视重磅开源！][YOLOX]

[新突破！Swin-UNet：基于纯 Transformer 结构的医学图像分割网络][Swin-UNet_ Transformer]

**-------------------**

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[pdf_Github2500]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247486763&idx=1&sn=da878ccafba79932be8eeca955697477&chksm=fcb71194cbc0988259f32b0b7a77fe7830c477c9c9a4fcb00f2e366c97d776574c2bc949e993&scene=21#wechat_redirect
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[cc2adb31ca4e78c9fc2533f90122fdda.png]: /images/20220828/5143d78db3a347ad9efce0d8489ed45b.png
[Hinton_Pix2Seq]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247521136&idx=2&sn=82175aa39cfc86425ff2510cce1310c8&chksm=fcb48fcfcbc306d9f7580beff30e1cbdbb759a675e26929f7cdeb094122a6d7d940b922b0a02&scene=21#wechat_redirect
[Anchor DETR_Transformer]: https://blog.csdn.net/Sophia_11/article/details/120465460
[TPH-YOLOv5_Transformer_Yolov5]: https://blog.csdn.net/Sophia_11/article/details/120446147
[Transformer_ICCV2021]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247520701&idx=2&sn=ce909619d50417c6353b740550126ec3&chksm=fcb48d02cbc30414f71967d9d83496db71344e0a393b80806186d732092a0c3ebfaa3d207b40&scene=21#wechat_redirect
[ICCV 2021]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247520601&idx=2&sn=f06f7deb121d774b8683ab5043bbdd53&chksm=fcb48de6cbc304f0c5a699798315ccf14ff3898308df8bb637852c8c7ab2461fb86cd27314ce&scene=21#wechat_redirect
[329]: https://blog.csdn.net/Sophia_11/article/details/120212914
[Fastformer_Transformer_MSRA]: https://blog.csdn.net/Sophia_11/article/details/120072544
[Paint Transformer_ICCV2021 Oral]: https://blog.csdn.net/Sophia_11/article/details/120386973
[Pure Transformer _ReID]: https://blog.csdn.net/gzq0723/article/details/119813919
[ResNet_CoTNet_Transformer]: https://blog.csdn.net/amusi1994/article/details/119363684
[Conformer_Transformer_CNN]: https://blog.csdn.net/Sophia_11/article/details/119860627
[ImageNet_Long-Short Transformer]: https://blog.csdn.net/Sophia_11/article/details/120375839
[SOTA_DetCo]: https://blog.csdn.net/qq_29462849/article/details/119123163
[YOLOX]: https://blog.csdn.net/Sophia_11/article/details/119012008
[Swin-UNet_ Transformer]: https://blog.csdn.net/amusi1994/article/details/116957208
[Kings]: https://blog.csdn.net/Sophia_11/article/details/114274846
[846478b49f7ab6dda4c8ce1883aefaed.png]: /images/20220828/92d8cac602224b3d8051ca5e5c94253d.png
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[Link 23]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247497362&idx=2&sn=5b3436738550a7e4d62c2bb98befe3df&chksm=fcb4ea2dcbc3633b228e5712ecfc9d302ff56b7d96e83232f1c7ffb0a8bd2ffc908b5003b9d9&scene=21#wechat_redirect
[_ _ _]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247498631&idx=2&sn=7a91e5850ec4885a4d7028b925e6f2d2&chksm=fcb4e738cbc36e2e8ed92fcc6acbf5024ee73d4f5b2e05bcd2c31333fad86d6d4638b06fef6f&scene=21#wechat_redirect
[Link 24]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247489104&idx=1&sn=189ff572dc14ee137e83e7e4e7a4cc9f&chksm=fcb70aefcbc083f9b351b1b29c56d70cc1748181583490c3769b77a495e74747b8f7491ee39a&scene=21#wechat_redirect
[Link 25]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247487741&idx=2&sn=40bbb83c4ecda43fcc11fc3eb3a7130f&chksm=fcb70c42cbc085545c4ccfd51563cc59e38134147803c0c59e0aa3827385f060ccc75b3df888&scene=21#wechat_redirect
[PyTorch_Tensor]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247504061&idx=1&sn=207d2bd4895e0b4e303c3d9d63511c72&chksm=fcb4cc02cbc3451449159333c651688e7d71c7a744a4e4e0d914fb14adb5ad4f7e122eff7e18&scene=21#wechat_redirect
[CNN_ _]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247503173&idx=1&sn=e92e9adb903e7a87d0b64ceff982020c&chksm=fcb4d1facbc358ec09986cd8b32ce222c3394c46e2f0bd60c282d63ffeea897e7211984e6fca&scene=21#wechat_redirect
[Link 26]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247502893&idx=1&sn=e5720b57b0804dca182552ba9d6c1eb3&chksm=fcb4d092cbc359841d6f5a9e3b838e3894d34b35d74a1627fa9bf37e27c019ca9d9af84c362d&scene=21#wechat_redirect
[Transformer _]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247502134&idx=1&sn=ed4a262db0be219feb017c08f2185779&chksm=fcb4d589cbc35c9f0156ac454dccb82a501f4b8129adbbcf94a3ca83d3af129f4ebadf173e75&scene=21#wechat_redirect
[Scaled-YOLOv4_ _]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247500056&idx=2&sn=95a04371dc0b49e8c862b03a88edbb8c&chksm=fcb4dda7cbc354b14c4cd4f63eadf02680112ee21015fe372cf3055f9b577b7dd989843eda1f&scene=21#wechat_redirect
[PyTorch_ _]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247497349&idx=3&sn=b4b1ea0af5d42c817bc776cab0a8ac28&chksm=fcb4ea3acbc3632cd61c21d40df0c8cd9e84603fcbe551ceea721e4d8c43959a3c74e929a0fe&scene=21#wechat_redirect
[PyTorch_ _ 1]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247497036&idx=2&sn=552069978cf317b9581b4c9a62a003e2&chksm=fcb4e9f3cbc360e51de7a7ceb3a6adde123e488d98a3baf4204270311ef609fa5510682ff29b&scene=21#wechat_redirect
[Numpy]: https://blog.csdn.net/dafeidouzi/article/details/100520906
[10]: https://blog.csdn.net/lvsehaiyang1993/article/details/80342473
[CVPR 2020]: https://blog.csdn.net/u013685264/article/details/104984809
[Link 27]: https://blog.csdn.net/fengdu78/article/details/107308594
[YOLOv4]: https://blog.csdn.net/fengdu78/article/details/106450074
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