We use the brainwash dataset introduced by paper End-to-end people detection in crowded scenes.
2019.09.23model v1 for brainwash dataset is released.
- v1 - is designed for brainwash dataset, covering head scale [10, 160]. It has 4 branches. Please check
./symbol_farm/symbol_structures.xlsxfor details.
- Platform info: NVIDIA RTX 2080TI, CUDA 10.0, CUDNN 7.4.2, TensorRT 5.1.5.0
| Model Version | 320×240 | 640×480 | 1280×720 | 1920×1080 | 3840×2160 | 7680×4320 |
|---|---|---|---|---|---|---|
| v1 | 0.83ms(1198.38FPS) | 1.91ms(524.14FPS) | 4.83ms(206.92FPS) | 10.62ms(94.19FPS) | 42.28ms(23.65FPS) | 166.81ms(5.99FPS) |
- Platform info: NVIDIA GTX 1060(laptop), CUDA 10.0, CUDNN 7.4.2, TensorRT 5.1.5.0
| Model Version | 320×240 | 640×480 | 1280×720 | 1920×1080 | 3840×2160 |
|---|---|---|---|---|---|
| v1 | 1.62ms(618.53FPS) | 4.83ms(207.06FPS) | 13.67ms(73.18FPS) | 30.01ms(33.32FPS) | 121.15ms(8.25FPS) |
CAUTION: The latency may vary even in the same setting.
We train v1 on the training set (10769 images with 81975 annotated heads) and evaluate on the test set (500 images with 5007 annotated heads). This dataset is relatively simple due to monotonous scenario.
Average Precision (AP) is used for measuring the accuracy. In detail, we use code Object-Detection-Metrics for calculating the AP metric. The following table presents the results:
| Method | AP |
|---|---|
| ReInspect, Lhungarian [1] | 0.78 |
| FCHD [2] | 0.70 |
| v1 (our) | 0.91 |
[1] End-to-end people detection in crowded scenes
[2] FCHD: Fast and accurate head detection in crowded scenes
The v1 significantly outperforms the existing methods.
Please refer to README in face_detection for details.