Name
Title | ||
|---|---|---|
Deep Background Subtraction of Thermal and Visible Imagery for Pedestrian Detection in Videos. |
Abstract | ||
|---|---|---|
In this paper, we introduce an efficient framework to subtract the background from both visible and thermal imagery for pedestrians’ detection in the urban scene. We use a deep neural network (DNN) to train the background subtraction model. For the training of the DNN, we first generate an initial background map and then employ randomly 5% video frames, background map, and manually segmented ground truth. Then we apply a cognition-based post-processing to further smooth the foreground detection result. We evaluate our method against our previous work and 11 recently widely cited method on three challenge video series selected from a publicly available color-thermal benchmark dataset OCTBVS. Promising results have been shown that the proposed DNN-based approach can successfully detect the pedestrians with good shape in most scenes regardless of illuminate changes and occlusion problem. |
Year | Venue | Field |
|---|---|---|
2018 | BICS | Background subtraction,Computer vision,Computer science,Foreground detection,Ground truth,Artificial intelligence,Artificial neural network,Pedestrian detection |
DocType | Citations | PageRank |
Conference | 1 | 0.34 |
References | Authors | |
16 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yijun Yan | 1 | 36 | 3.55 |
| Huimin Zhao | 2 | 206 | 23.43 |
| Fu-Jen Kao | 3 | 1 | 0.34 |
| Valentin Masero Vargas | 4 | 1 | 0.34 |
| Sophia Zhao | 5 | 2 | 1.41 |
| Jinchang Ren | 6 | 1144 | 88.54 |