A master thesis from Aalborg University
Upper Body Pedestrian Detection
Author(s)
Term
4. term
Publication year
2016
Submitted on
2016-06-02
Pages
55 pages
Abstract
Trac accidents involving pedestrians are a very serious matter as it has one of the highest risks to end with fatalities. Therefore, a program is needed to help detect pedestrians in a trac scenario. In addition the system will be designed to be able to handle the most common occlusion problem, which is the lower part of the body and is therefore designed as an upper body detector. The proposed upper body detector in this project is a CNN based system with three CNNs in a cascade. This is done in order to not only have a good performance, but also a system which is computationally lighter than a single deep CNN for possible real life scenario use. The cascade consist rstly of a very shallow CNN followed by a not as shallow CNN and lastly a deeper CNN for nal predictions. After the conducted experiments, it is shown that the performance of this upper body detector does not perform as well as a full-sized pedestrian detector. This was expected as the upper body is a subset of the full-sized pedestrian, but the results are bad enough to not be usable in a real life scenario. For that to happen further research on this topic is needed for improving on the feature extraction of upper bodies.
Keywords
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