STEP-ICCV21 Results
Click on a measure to sort the table accordingly. See below for a more detailed description.
| Tracker | STQ | AQ | SQ (IoU) | Hz |
| IPL_ETRI 1. | 48.6 | 43.3 | 54.5 | 9.5 |
| Y. Wang, H. Zhang, Z. Jiang, J. Mei, C. Yang, J. Cai, J. Hwang, K. Kim, P. Kim. HVPS: A Human Video Panoptic Segmentation Framework. In , . | ||||
| EffPS_MM 2.  | 42.8 | 26.4 | 69.2 | 1.9 |
| R. Mohan, A. Valada. Efficientps: Efficient panoptic segmentation. In International Journal of Computer Vision, 2021. | ||||
| EffPS_MM 3. | 42.8 | 26.4 | 69.2 | 1.9 |
| R. Mohan, A. Valada. Efficientps: Efficient panoptic segmentation. In International Journal of Computer Vision, 2021. | ||||
| siain 4. | 31.8 | 15.4 | 65.7 | 3.2 |
| J. Ryu, K. Yoon. An End-to-End Trainable Video Panoptic Segmentation Method usingTransformers. In , 2021. | ||||
| Sequences | Frames |
| 2 | 950 |
Evaluation Measures
Lower is better. Higher is better.| Measure | Better | Perfect | Description |
| STQ | higher | 100% | Segmentation and Tracking Quality [1]. The geometric mean of Association Quality and Segmentation Quality. |
| AQ | higher | 100% | Association Quality [1]. The class-agnostic Association Quality. |
| SQ (IoU) | higher | 100% | Segmentation Quality [1]. The mean IoU of the semantic segmentation. |
| Hz | higher | Inf. | Processing speed (in frames per second excluding the detector) on the benchmark. The frequency is provided by the authors and not officially evaluated by the MOTChallenge. |
Legend
| Symbol | Description |
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This is an online (causal) method, i.e. the solution is immediately available with each incoming frame and cannot be changed at any later time. |
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This method used the provided detection set as input. |
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This method used a private detection set as input. |
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This entry has been submitted or updated less than a week ago. |
