Paper Info
Title
Detecting pedestrians in surveillance videos based on convolutional neural network and motion.
Abstract
Pedestrian detection is a fundamental computer vision task with many practical applications in robotics, video surveillance, autonomous driving, and automotive safety. However, it is still a challenging problem due to the tremendous variations in illumination, clothing, color, scale, and pose. The aim of this paper to present our dynamic pedestrian detector. In this paper, we propose a pedestrian detection approach that uses convolutional neural network (CNN) to differentiate pedestrian and non-pedestrian motion patterns. Although the CNN has good generalization performance, the CNN classifier is time-consuming. Therefore, we propose a novel architecture to reduce the time of feature extraction and training. Occlusion handling is one of the most important problem in pedestrian detection. For occlusion handling, we propose a method, which consists of extensive part detectors. The main advantage of our algorithm is that it can be trained on weakly labeled data, i.e. it does not require part annotations in the pedestrian bounding boxes.
Year
Venue
Field
2016
European Signal Processing Conference
Computer vision,Pedestrian,Convolutional neural network,Computer science,Feature extraction,Artificial intelligence,Classifier (linguistics),Detector,Pedestrian detection,Robotics,Bounding overwatch
DocType
ISSN
Citations 
Conference
2076-1465
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Domonkos Varga1134.29
Tamás Szirányi215226.92