Detection and Tracking of Moving Objects from Overlapping EO and IR Sensors
Abstract
We present an approach for tracking moving objects observed by EO and IR
sensors on a moving platform. Our approach detects and tracks the moving
objects after accurately recovering the geometric relationship between
different sensors. We address the tracking problem by separately modeling
the appearance and motion of the moving regions using stochastic models. The
appearance of the detected blobs is described by multiple spatial
distribution models of blobs' colors and edges from different sensors. This
representation is invariant to 2D rigid and scale transformation. It
provides a rich description of the object being tracked and produces an
accurate blob similarity measure for tracking - especially when one of
sensors fails to provide reliable information. The motion model is obtained
using a Kalman Filter (KF) process, which predicts the position of the
moving objects while taking into account the camera motion. Tracking is
performed by the maximization of a joint probability model reflecting
appearance and motion. The novelty of our approach consists in defining a
Joint Probability Data Association Filter (JPDAF) for integrating multiple
cues from multiple sensors, It provides an unified framework for fusing
information from different types of sensors. The proposed method tracks
multiple moving objects with partial and total occlusions under various
illumination conditions. We demonstrate the performance of the system on
several real video surveillance sequences.