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Unicorn

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`@@ -66,6 +66,9 @@ Multiple Object Tracking in Deep Learning Approaches:A Survey [[paper](https://w`
`66`	`66`
`67`	`67`	`<a id="markdown-2022" name="2022"></a>`
`68`	`68`	`### 2022`
	`69`	`+`
	`70`	`+Unicorn: Towards Grand Unification of Object Tracking [[code](https://github.com/MasterBin-IIAU/Unicorn)] [[paper](https://arxiv.org/pdf/2207.07078.pdf)] ECCV2022 Oral`
	`71`	`+`
`69`	`72`	`P3AFormer: Tracking Objects as Pixel-wise Distributions [[code](https://sjtuytc.github.io/zelin_pages/p3aformer.html)] [[paper](https://arxiv.org/pdf/2207.05518.pdf)] ECCV2022 oral`
`70`	`73`
`71`	`74`	`BoT-SORT: BoT-SORT: Robust Associations Multi-Pedestrian Tracking [[code](https://github.com/NirAharon/BOT-SORT)] [[paper](https://arxiv.org/pdf/2206.14651.pdf)]`

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`@@ -50,6 +50,8 @@ Multiple Object Tracking in Deep Learning Approaches:A Survey [[paper](https://w`
`50`	`50`	`<a id="markdown-2022" name="2022"></a>`
`51`	`51`	`### 2022`
`52`	`52`
	`53`	`+Unicorn: Towards Grand Unification of Object Tracking [[code](https://github.com/MasterBin-IIAU/Unicorn)] [[paper](https://arxiv.org/pdf/2207.07078.pdf)] ECCV2022 Oral`
	`54`	`+`
`53`	`55`	`P3AFormer: Tracking Objects as Pixel-wise Distributions [[code](https://sjtuytc.github.io/zelin_pages/p3aformer.html)] [[paper](https://arxiv.org/pdf/2207.05518.pdf)] ECCV2022 oral`
`54`	`56`
`55`	`57`	`BoT-SORT: BoT-SORT: Robust Associations Multi-Pedestrian Tracking [[code](https://github.com/NirAharon/BOT-SORT)] [[paper](https://arxiv.org/pdf/2206.14651.pdf)]`

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