Detection Challenge Results

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 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
2 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
3 R-FCN1 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 Advances in neural information processing systems(NIPS), 2016
4 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. In IEEE International Conference on Multimedia and Expo (ICME), 2017.
5 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
6 NANO 63.01% 80.33% 68.04% 50.73% 67.00% 62.20% 55.89% 73.89% - -
Anonymous submission
7 FasterRCNN2 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
8 YOLO2 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
9 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]
10 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]
11 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]
12 SA-FRCNN 45.83% 73.93% 49.00% 30.76% 49.97% 52.30% 33.39% 55.04% - -
Anonymous submission
13 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]