Exploiting Inter-View Correlation for Bandwidth-Efficient Data Gathering in Wireless Multi-Camera Networks

• Main Authors: Chang-Yu Song, 宋長諭
• Other Authors: 謝宏昀
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/59220995283443303629

碩士 === 國立臺灣大學 === 電信工程學研究所 === 103 === In this thesis, we investigate the problem of correlated data gathering in the wireless multi-camera networks by considering the I-frame selection problem and the P-frame association problem. Since multiple cameras may be deployed in a neighborhood area with overlapping perspectives of the street views, we exploit the capability of transmission overhearing among cameras. If a camera can overhear transmissions from previous scheduled nearby cameras, it can reference the image and reduce the amount of bits required to be delivered to the aggregator by performing the multiview encoding technique. Unlike related works often use geometric information to predict correlation among cameras, we refer to the multiview video encoder for measuring realistic cameras correlation such that no performance loss will be caused due to prediction error. We further propose three I-frame selection algorithms based on branch-and-bound, simulated annealing, and graph approximation. We also introduce a P-frame association method to determine reference structure for all cameras such that the amount of required transmission bits can be minimized. Besides, for real-world applications, it might require multiple transmission rounds for delivering the collected images back to the data aggregator. Therefore, in this thesis, we also describe how to apply the correlated data gathering scheme via overhearing source coding for more than one transmission rounds. To evaluate the proposed algorithms, we resort to a 3D modeling software to generate quasi-realistic city views for all cameras and use a H.264 multiview video encoding reference software to encode collected images. Based on the evaluation for a semi-realistic multi-camera network, we compare the performance gain of our three proposed algorithms with a baseline approaches and point out the trade-off among the three proposed methods. That is, the graph approximation algorithm can perform well when the network is high correlated, whereas the simulated annealing algorithm might be a better choice if the network correlation level becomes low. We also show that our proposed approaches can result in 35% transmission reduction for high correlated multi-camera networks, however, only 15% can be reduced if geometric correlation is applied. The results thus motivate further investigation along this direction.