Abstract

We propose a method for obtaining the vision graph for a distributed camera network, in which each camera is represented by a node, and an edge appears between two nodes if the two cameras jointly image a sufficiently large part of the environment. The technique is decentralized, requires no ordering on the set of cameras, and assumes that cameras can only communicate a finite amount of information with each other in order to establish the vision graph. Each camera first detects a large number of feature points that are approximately scale- and viewpoint-invariant. Both the number of features and the length of each feature descriptor are substantially reduced to form a fixed-length “feature digest” that is broadcast to the rest of the network. Each receiver camera decompresses the feature digest to recover approximate feature descriptors, robustly estimates the epipolar geometry to reject incorrect matches and grow additional ones, and decides whether sufficient evidence exists to form a vision graph edge. We use receiver-operating-characteristics (ROC) curves to analyze the performance of different message formation schemes, and show that high detection rates can be achieved while maintaining low false alarm rates. Finally, we show how a camera calibration algorithm that passes messages only along vision graph edges can recover accurate 3D structure and camera positions in a distributed manner. We demonstrate the accurate performance of the vision graph generation and camera calibration algorithms using a simulated 60-node outdoor camera network. In this simulation, we achieved vision graph edge detection probabilities exceeding 0.8 while maintaining false alarm probabilities below 0.05. I.

Citations