TY - GEN
T1 - Deep Background Subtraction of Thermal and Visible Imagery for Pedestrian Detection in Videos
AU - Yan, Yijun
AU - Zhao, Huimin
AU - Kao, Fu Jen
AU - Vargas, Valentin Masero
AU - Zhao, Sophia
AU - Ren, Jinchang
N1 - Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Deep neural network (DNN)
KW - Pedestrian detection
KW - Video salient objects
UR - http://www.scopus.com/inward/record.url?scp=85055100212&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00563-4_8
DO - 10.1007/978-3-030-00563-4_8
M3 - Conference contribution
AN - SCOPUS:85055100212
SN - 9783030005627
T3 - Lecture Notes in Computer Science
SP - 75
EP - 84
BT - Advances in Brain Inspired Cognitive Systems
A2 - Hussain, Amir
A2 - Luo, Bin
A2 - Zheng, Jiangbin
A2 - Zhao, Xinbo
A2 - Liu, Cheng-Lin
A2 - Ren, Jinchang
A2 - Zhao, Huimin
PB - Springer
CY - Switzerland
T2 - 9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018
Y2 - 7 July 2018 through 8 July 2018
ER -