<div>
<div>
<div>
<p>With the advancement in Unmanned Aerial Vehicles (UAV) technology, UAVs have become
accessible to the public. However, recent world events have highlighted that the rapid increase of
UAVs is bringing with it a threat to public privacy and security. Thus, it is important to think
about how to prevent the threats of UAVs to protect our privacy and safety. This study aims to
provide an alternative way to substitute an expensive system by using 2D optical sensors that can
be easily utilized by people. One of the main challenges for aerial object recognition with
computer vision is discriminating other flying objects from the targets, in the far distance. There
are limitation to classify the flying object when it appears as a set of small black pixels on the
frame. The movement feature can help the system to extract the discriminative feature, so that the
classifier can classify the UAV and other objects, such as a bird. Thus, this study proposes a drone
detection system using two elements of information, which are appearance information and
motion information to overcome the limitation of a vision based system.
</p>
</div>
</div>
</div>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12492935 |
| Date | 17 June 2020 |
| Creators | Eunsuh Lee (8981213) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/DRONE_CLASSIFICATION_WITH_MOTION_AND_APPEARANCE_FEATURE_USING_CONVOLUTIONAL_NEURAL_NETWORKS/12492935 |
Page generated in 0.0027 seconds