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Merge pull request luanshiyinyang#10 from sjtuytc/UpdateP3AFormer

Update P3AFormer - ECCV Oral

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`@@ -65,6 +65,7 @@ Multiple Object Tracking in Deep Learning Approaches:A Survey [[paper](https://w`
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`66`	`66`	`<a id="markdown-2022" name="2022"></a>`
`67`	`67`	`### 2022`
	`68`	`+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`
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`69`	`70`	`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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`@@ -49,6 +49,9 @@ Multiple Object Tracking in Deep Learning Approaches:A Survey [[paper](https://w`
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`50`	`50`	`<a id="markdown-2022" name="2022"></a>`
`51`	`51`	`### 2022`
	`52`	`+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`
	`53`	`+`
	`54`	`+Simple Cues Lead to a Strong Multi-Object Tracker [[code]()] [[paper](https://arxiv.org/pdf/2206.04656.pdf)]`
`52`	`55`
`53`	`56`	`BoT-SORT: BoT-SORT: Robust Associations Multi-Pedestrian Tracking [[code](https://github.com/NirAharon/BOT-SORT)] [[paper](https://arxiv.org/pdf/2206.14651.pdf)]`
`54`	`57`

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