Aerial moving target detection based on motion vector field analysis
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Robust tracking in aerial imagery based on an ego-motion Bayesian model
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
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In this paper we propose a system that detects independently moving objects (IMOs) in forward looking infra-red (FLIR) image sequences taken from an airborne, moving platform. Ego-motion effects are removed through a robust multi-scale affine image registration process. Consequently, areas with residual motion indicate object activity. These areas are detected, refined and selected using a Bayes' classifier. The remaining regions are clustered into pairs. Each pair represents an object's front and rear end. Using motion and scene knowledge we estimate object pose and establish a region-of-interest (ROI) for each pair. Edge elements within each ROI are used to segment the convex cover containing the IMO. We show detailed results on real, complex, cluttered and noisy sequences. Moreover, we outline the integration of our robust system into a comprehensive automatic target recognition (ATR) and action classification system.