Elastic Grid-Based Multi-Fovea Algorithm for Real-Time Object-Motion Detection in Airborne Surveillance

Abstract

In this chapter, a generic multi-fovea video processing architecture is presented, which supports a broad class of algorithms designed for real-time motion detection in moving platform surveillance. The various processing stages of these algorithms can be decomposed into three classes: computationally expensive calculations can be focused onto multiple foveal regions that are selected by a preprocessing step running on a highly parallel topological array and leaving only the nontopological (typically vector-matrix) computations to be executed on serial processing elements. The multi-fovea framework used in this chapter is a generalized hardware architecture enabling an efficient partitioning and mapping of different algorithms with enough flexibility to achieve good compromise in the design tradeoff between computational complexity versus output quality. We introduce and compare several variants of four different classes of state-of-the-art algorithms in the field of independent motion analysis and detection. On the basis of the analysis, we propose a new algorithm called the Elastic Grid Multi-Fovea Detector characterized by moderate hardware complexity while maintaining competitive detection quality.