Detection challenge results are presented below. The plot results of detectors can be found here.
The Precision-Recall curve of the submission can be drawn compared to the state-of-the-art detectors here.
| Method | Overall | Easy | Medium | Hard | Cloudy | Night | Rainy | Sunny | Speed | Environment | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | SpotNet | 86.80% | 97.58% | 92.57% | 76.58% | 89.38% | 89.53% | 80.93% | 91.42% | 14 fps (python) | CPU:Intel i7-6850K 3.6GHZ, RAM:32GB, GPU:GTX1080 Ti |
| Hughes Perreault, Guillaume-Alexandre Bilodeau, Nicolas Saunier and Maguelonne Héritier. SpotNet: Self-Attention Multi-Task Network for Object Detection. arXiv 2020. Polytechnique Montreal, Genetec, Canada | |||||||||||
| 2 | SSD_VDIG | 82.68% | 94.60% | 89.71% | 70.65% | 89.81% | 83.02% | 73.35% | 88.11% | 2 fps | GPU:2x Titan X |
| PKU & Alibaba AI Labs | |||||||||||
| 3 | ME-Net | 80.76% | 94.56% | 85.90% | 69.72% | 87.19% | 80.68% | 71.06% | 89.74% | 14 fps (Python) | CPU:8x Intel i7-4790 3.6GHZ, RAM:15.5GB, GPU:GTX1080 |
| CASIA & Visystem | |||||||||||
| 4 | HAVD | 80.51% | 94.48% | 86.13% | 69.02% | 87.28% | 82.30% | 69.37% | 89.71% | 2.1 fps | CPU:2x Intel Xeon E5-2650v4 2.4GHZ, GPU:Titan X |
| THU CV-AI Lab | |||||||||||
| 5 | FG-BR_Net | 79.96% | 93.49% | 83.60% | 70.78% | 87.36% | 78.42% | 70.50% | 89.89% | 10 fps (Python) | GPU:Tesla M40 |
| Zhihang Fu, Yaowu Chen, Hongwei Yong, Rongxin Jiang, Lei Zhang and Xian-Sheng Hua. Foreground Gating and Background Refining Network for Surveillance Object Detection. TIP 2019. | |||||||||||
| 6 | DCN | 79.85% | 93.85% | 85.07% | 69.00% | 85.55% | 82.38% | 68.95% | 89.08% | 8 fps (Python) | CPU:2x Intel Xeon E5-2650v4 2.4GHZ, GPU:Titan X |
| Anonymous submission | |||||||||||
| 7 | HAT | 78.64% | 93.44% | 83.09% | 68.04% | 86.27% | 78.00% | 67.97% | 88.78% | 3.6 fps (C++,Python) | GPU:Titan X |
| Shuzhe Wu, Meina Kan, Shiguang Shan and Xilin Chen. Hierarchical Attention for Part-Aware Face Detection. IJCV 2019. | |||||||||||
| 8 | GP-FRCNNm | 77.96% | 92.74% | 82.39% | 67.22% | 83.23% | 77.75% | 70.17% | 86.56% | 4 fps (C++, Python) | CPU:Intel(R) Xeon(R) CPU E5-2690v3 2.6GHZ, RAM:256GB, GPU:K40 |
| Sikandar Amin, Fabio Galasso. Geometric Proposals for Faster R-CNN. AVSS 2017. | |||||||||||
| 9 | CSP | 77.67% | 93.65% | 83.67% | 64.54% | 86.81% | 80.63% | 61.39% | 89.66% | 4 fps (C++, Python) | CPU:Intel(R) Xeon(R) CPU E5-2690v3 2.6GHZ, RAM:256GB, GPU:K40 |
| Wei Liu, Shengcai Liao, Weiqiang Ren, Weidong Hu, and Yinan Yu. High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection. CVPR 2019. | |||||||||||
| 10 | RTN | 74.15% | 91.52% | 79.16% | 61.73% | 77.02% | 77.20% | 65.27% | 84.14% | 19.61 fps (C++) | 2x Intel Xeon E5-2620v4, RAM:128GB,GPU:GTX1080 |
| Anonymous submission | |||||||||||
| 11 | HPNDFCN | 71.56% | 93.51% | 78.00% | 55.62% | 78.84% | 79.37% | 57.22% | 84.92% | 5 fps | Intel(R) i5 2 core 2.10GHz, RAM:128GB,GPU:GTX1080Ti |
| Anonymous submission | |||||||||||
| 12 | R-FCN | 69.87% | 93.32% | 75.67% | 54.31% | 74.38% | 75.09% | 56.21% | 84.08% | 6 fps (Python) | 2x Intel Xeon E5-2650v4, RAM:128GB,GPU:TitanX |
| Dai, J., Li, Y., He, K., & Sun, J. R-fcn: Object detection via region-based fully convolutional networks. In NeurIPS 2016. | |||||||||||
| 13 | ASDN-EB | 69.60% | 89.87% | 75.09% | 55.69% | 75.27% | 74.62% | 54.85% | 84.79% | 7.69 fps | CPU: 2x Intel Xeon E5-2650v4 2.4GHZ, GPU: Titan X |
| Anonymous submission | |||||||||||
| 14 | EB | 67.96% | 89.65% | 73.12% | 53.64% | 72.42% | 73.93% | 53.40% | 83.73% | 10.00 fps (C++) | 1x GPU:TitanX |
| Li Wang, Yao Lu, Hong Wang, Yingbin Zheng, Hao Ye, Xiangyang Xue, Evolving Boxes for Fast Vehicle Detection. ICME, 2017. | |||||||||||
| 15 | LateralCNN | 67.25% | 89.56% | 73.59% | 51.61% | 69.11% | 74.36% | 55.77% | 78.66% | 21.47 fps | Intel(R) i7-6800K 6 core 3.40GHz, RAM:32GB,GPU:TitanX |
| Anonymous submission | |||||||||||
| 16 | NANO | 63.01% | 80.33% | 68.04% | 50.73% | 67.00% | 62.20% | 55.89% | 73.89% | - | - |
| Anonymous submission | |||||||||||
| 17 | Faster R-CNN | 58.45% | 82.75% | 63.05% | 44.25% | 66.29% | 69.85% | 45.16% | 62.34% | 11.11 fps (C++) | 1x GPU:TitanX |
| Anonymous submission | |||||||||||
| 18 | YOLOv2 | 57.72% | 83.28% | 62.25% | 42.44% | 57.97% | 64.53% | 47.84% | 69.75% | - | 2x Intel Xeon E5-2620v4, RAM:128GB,GPU:GTX1080 |
| Anonymous submission | |||||||||||
| 19 | RN-D-from-scratch | 54.69% | 80.98% | 59.13% | 39.23% | 59.88% | 54.62% | 41.11% | 77.53% | 12 fps (Python) | Intel i7-6850K @3.60GHz, RAM:32GB, GPU:GTX 1080 Ti |
| H. Perreault, G.-A. Bilodeau, N. Saunier and P. Gravel. Road User Detection in Videos. In arXiv, 2019. | |||||||||||
| 20 | CompACT | 53.23% | 64.84% | 58.70% | 43.16% | 63.23% | 46.37% | 44.21% | 71.16% | 0.22 fps (Matlab,C++) | 2x Intel Xeon E5-2470v2 @2.40GHz, RAM:64GB, GPU:Tesla K40 |
| Z. Cai, M. Saberian, and N. Vasconcelos. Learning complexity-aware cascades for deep pedestrian detection. In ICCV, 2015. [code] | |||||||||||
| 21 | R-CNN | 48.95% | 59.31% | 54.06% | 39.47% | 59.73% | 39.32% | 39.06% | 67.52% | 0.10 fps (Matlab,C++) | 2x Intel Xeon E5-2470v2 @2.40GHz, RAM:64GB, GPU:Tesla K40 |
| R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, pages 580-587, 2014. [code] | |||||||||||
| 22 | ACF | 46.35% | 54.27% | 51.52% | 38.07% | 58.30% | 35.29% | 37.09% | 66.58% | 0.67 fps (Matlab,C++) | 2x Intel Xeon E5-2470v2 @2.40GHz, RAM:64GB |
| P. Dollár, R. Appel, S. Belongie, and P. Perona. Fast feature pyramids for object detection. In TPAMI, 36(8):1532-1545, 2014. [code] | |||||||||||
| 23 | SA-FRCNN | 45.83% | 73.93% | 49.00% | 30.76% | 49.97% | 52.30% | 33.39% | 55.04% | - | - |
| Anonymous submission | |||||||||||
| 24 | DPM | 25.70% | 34.42% | 30.29% | 17.62% | 24.78% | 30.91% | 25.55% | 31.77% | 0.17 fps (Matlab,C++) | 4x Intel Core i7-6600U @2.60GHz, RAM:8GB |
| P. F. Felzenszwalb, R. B. Girshick, D. A. McAllester, and D. Ramanan. Object detection with discriminatively trained part-based models. In TPAMI, 32(9):1627-1645, 2010. [code] | |||||||||||
