視覚センサネットワークにおけるスケーラブルな視野接続推定

摘要

This paper describes a scalable method of estimating a vision graph, in which a pair of camera nodes are connected by an edge if the two nodes share the same field of view, based on local image feature correspondences. The proposed method is imeplemented in a distributed fashion, meanwhile avoiding the flooding of the image feature information since it can be a bottleneck in achieving scalability. The key idea is to partition the image feature space into a set of disjoint regions so that the correspondence search can be carried out within a partitioned region, with each region served by a different network node independently. Simulated results using real images show that the proposed method achieves reasonable estimation performance while improving the traffic amount and traffic balance greatly.%本論文では，多数の視覚センサ間で，ある2つのセンサが共通の事象を観測しているかどうかという視野接続関係を，画像特徴量に基づいて推定する方法について検討する.従来研究ではノード数が増加すると画像特徴量のデータ量が膨大になりネットワークの負荷が大きくなるという問題があった.本論文では特徴量空間を分割し対応づけの計算をノードごとに分散させて行うことで，データ通信量を大幅に低減しながら十分な精度で視野接続推定が可能であることを示す.