Analysis of Brightness Transfer Function for Matching Targets across Networked Cameras

Abstract

In this paper we present an analysis of the intercamera matching problem based on colour and intensity of targets. Brightness Transfer Function (BTF) have been quite effective in matching targets across cameras with nonoverlapping fields of view. The BTF effectively compensates for the illumination changes in the environment and radiometric distortions due to difference in camera sensors. We used the Cumulative Brightness Transfer Function (CBTF) to model the relationship of separate radiometric responses of the cameras and difference in scene illumination. No shape assumption on the CBTF is made. A non-parametric and non-linear CBTF is computed from training data, where the identity of the targets across cameras are known. We studied the effect of the use of different shortest path algorithms in the computation of the BTF. Furthermore we analysed the effect of quantisation on the CBTF and its further effect on matching of the targets. We used the Bhattacarya distance to compute the match measure for reference and candidate transformed histograms. The effect of quantization is studied by quantitative evaluation in terms of temporal analysis, mean square error, and finally as Receiver Operating Characteristic (ROC) plots for matching targets across cameras in a network of cameras.

Fusion of colour and facial features for person matching in a camera network

“…In our experiments we have used Bellman Ford single source shortest path algorithms as described in [15] and implemented in [16]. We have used the Bellman Ford shortest path algorithm because by empirical analysis it has been found to be more efficient than Dijkstras algorithm [17]. Although theoretically the computational complexity of Dijkstras algorithm is O(V log2V ), and is better than that of Bellman Ford O(V E) where V is the number of vertices and E the number of edges in the graph.…”