Figure 72: SLAM Results on a original video and an anonymized video
Figure 73: Our face-anonymizing system results on drone 5.5.4 Our Face-Anonymizing System Result on Drone
Fig. 73 shows our face-anonymizing system results on a video recorded in a drone. The drone is hovering in our laboratory, and a person is walking in front of the drone. From this result, we can confirm that our system can well anonymize a face with varying its size.
Fig. 74 presents the comparison of feature extraction results on an original video and a anonymized video. Both videos confirm that the feature points are well extracted. In our anonymization scheme, the extracted feature points are almost the same as that of the original video.
Figure 74: SLAM results on drone
modified generators’ losses of those GANs for our purpose. In our system, our face-anonymizing networks transform all original faces in every snapshot to different faces. Hence, in snapshots, privacy of people can be fundamentally protected. Via various test dataset, we confirmed that our system can indeed preserve the person’s privacy qualitatively and quantitatively. In addition, by implementing ORB-SLAM2, we also verified that our system can preserve the vision-based perception of drone with well-anonymized faces. Finally, our system was also evaluated with actually recorded videos on drone.
VI Conclusion
This dissertation deals with two key components of a robot: wireless communication and vision.
For wireless communication, I focus on the asymmetry in setting the energy detection thresh- old of two different wireless communication systems. I figure out and define the asymmetric hidden terminals problem, where a wireless communication can be hidden from another wireless communication while the inverse is not true. Specifically, in wireless communication, the new phenomenon occurs by introducing LTE-LAA to unlicensed bands. Since Wi-Fi has been per- vasive in the unlicensed bands for decades, LTE-LAA should coexist with Wi-Fi. By designing each Markov chain of LTE-LAA and Wi-Fi and by combining two Markov chains with the joint Markov chain, I analyze the performance of LTE-LAA and Wi-Fi in terms of throughput and delay. The analysis presents that the asymmetric hidden terminals problem decreases through- put of LTE-LAA and Wi-Fi and also increases delay of both wireless communication systems.
In addition, I develop several solutions to alleviate the asymmetric hidden terminals problem by using power allocation and transmit precoding. The solutions allow LTE-LAA to avoid col- lision with Wi-Fi, and thus enhance the throughput and delay of LTE-LAA while improving or preserving the performance of Wi-Fi.
For robot vision, I develop a privacy-protection drone patrol system using face-anonymizing networks. Drone can go anywhere with high-resolution cameras, and indiscriminate shooting can be a crucial problem for people who are disinclined to be exposed and highly regard their privacy.
I propose a training architecture consisting of a segmentation learning part and a synthesis learning part. Each learning part is constructed by adopting two latest GANs, CycleGAN and GauGAN. In addition, generators’ losses are modified for my system purpose. With various test dataset, it is confirmed that our privacy-preserving drone vision system is able to deform human faces and thus can protect the person’s privacy. In addition, the proposed system preserves the vision-based perception of a drone with removal of the person’s privacy. Finally, I demonstrate the privacy-preserving vision system with a video recorded on a drone, which confirms that our system works well with various sizes of a face.
Figure 75: Test Results on FaceForensic Dataset via Algorithm 4
Figure 76: Test Results on FaceForensic Dataset via Algorithm 4
Figure 77: Additional Test Results on CelebA-HQ Dataset
Figure 78: Additional Test Results on Helen Dataset
Figure 79: Additional Test Results on FaceScrub Dataset
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