People Detection benchmark repository
Results
Authors: Alvaro Garcia Martin .
Contact university/company: Universidad Autonoma de Madrid.
Contact email: alvaro.garcia@uam.es
Method name: Edge.
Description and pocessing time: The Edge detector combines segmentation and exhaustive search in order to achieve robustness and real time operation. The Edge approach is implemented in C++ (OpenCV) and the computational cost is around 0.02 seconds per frame with 352x288 images. The tests have been performed on a Pentium IV with a CPU frequency of 2.4 GHz and 3GB RAM.
Parameters: Author default parameters [1].
You can download the people detection results of this method by clicking here.
Results for each video sequence:
Video |
Edge |
1 |
84.5 |
2 |
90.2 |
3 |
71.7 |
4 |
5.4 |
5 |
7.1 |
6 |
31.7 |
7 |
7.2 |
8 |
21.4 |
9 |
74.4 |
10 |
14.3 |
11 |
33.7 |
12 |
70.5 |
13 |
59.3 |
14 |
46.9 |
15 |
41.2 |
16 |
60.9 |
Average AUC |
45.0 |
Ranking |
4.00 |
Results for each background complexity:
Background complexity |
Edge |
Baseline |
82.1 |
Dynamic Background |
6.3 |
Camera Jitter |
19.4 |
Intermittent Object Motion |
35.9 |
Shadow |
55.8 |
Average AUC |
39.9 |
Ranking |
3.60 |
Results for each classification complexity:
Classification complexity |
Edge |
Low |
73.3 |
Medium |
37.9 |
High |
21.5 |
Average AUC |
44.2 |
Ranking |
4.33 |
Associated references:
[1] A. Garcia-Martin and J. M. Martinez, “Robust real time moving people detection in surveillance scenarios,” in Proc. of AVSS, 2010, pp. 241–247.
