Publication Type:

Conference Paper

Source:

Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, Institute of Electrical and Electronics Engineers Inc., p.838-842 (2019)

ISBN:

9781538675953

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065568285&doi=10.1109%2fICCSP.2019.8698101&partnerID=40&md5=bc08171ee89e9db37bfc65253013473f

Keywords:

Automobile drivers, Color, Deep learning, Driver assistance, FRCNN, ITS applications, Luminance, Neural networks, Night time, Pedestrian detection, Signal processing, State of the art, Thermal, Various illumination conditions

Abstract:

Multispectral pedestrian detection is becoming increasingly important in the field of computer vision due to its applications in driver assistance, surveillance, and monitoring. In this paper, we propose a brightness aware model for pedestrian detection using deep learning. A novel brightness aware mechanism depicts various illumination conditions, so as to enable prediction of day/ night scenario. Based on the detection of the brightness aware mechanism, a color or thermal model is used to detect pedestrians under day or night conditions respectively. The proposed method trained on FLIR-ADAS Thermal dataset and PASCAL VOC Color dataset, has achieved a mAP of '81.27%', which outperforms the current state of the art.

Notes:

cited By 0; Conference of 8th IEEE International Conference on Communication and Signal Processing, ICCSP 2019 ; Conference Date: 4 April 2019 Through 6 April 2019; Conference Code:147623

Cite this Research Publication

K. N. R. Chebrolu and Dr. (Col.) Kumar P. N., “Deep Learning based Pedestrian Detection at all Light Conditions”, in Proceedings of the 2019 IEEE International Conference on Communication and Signal Processing, ICCSP 2019, 2019, pp. 838-842.