Real-time pedestrian detection for driver assistance systems based on deep learning

Zhenfei Gong; Xinyu Wang; Wenbing Tao

doi:10.1117/12.2503047

9 August 2018 Real-time pedestrian detection for driver assistance systems based on deep learning

Zhenfei Gong, Xinyu Wang, Wenbing Tao

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108062V (2018) https://doi.org/10.1117/12.2503047
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

Abstract

We present a model for real-time pedestrian detection based on a deep learning framework. With respect to the base network for feature extraction, we have improved the network based on Mobilenet which is a simple and fast convolutional neural network. We only use the front part of its network and then build several new multi-scale convolutional layers to calculate multi-scale feature maps. With respect to the detection network behind the feature extraction, we use a simplified SSD(single shot multibox detector) model to detect pedestrians with fewer feature maps. In addition, we design detection boxes with specific sizes according to pedestrian’s shape characteristics. To avoid overfitting, we apply data augmentation and dropout techniques to training. Experimental results on PASCAL VOC and KITTI confirm that the speed of our detection model has been increased by 22.2% while precision remains almost unchanged. Our approach makes a trade-off between speed and precision, and has an obvious speed advantage over other detection approaches.

Citation Download Citation

Zhenfei Gong, Xinyu Wang, and Wenbing Tao "Real-time pedestrian detection for driver assistance systems based on deep learning", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108062V (9 August 2018); https://doi.org/10.1117/12.2503047

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available