  It is an important task to reliably detect and track multiple moving objects
for video surveillance and monitoring. However, when occlusion occurs in
nonlinear motion scenarios, many existing methods often fail to continuously
track multiple moving objects of interest. In this paper we propose an
effective approach for detection and tracking of multiple moving objects with
occlusion. Moving targets are initially detected using a simple yet efficient
block matching technique, providing rough location information for multiple
object tracking. More accurate location information is then estimated for each
moving object by a nonlinear tracking algorithm. Considering the ambiguity
caused by the occlusion among multiple moving objects, we apply an unscented
Kalman filtering (UKF) technique for reliable object detection and tracking.
Different from conventional Kalman filtering (KF), which cannot achieve the
optimal estimation in nonlinear tracking scenarios, UKF can be used to track
both linear and nonlinear motions due to the unscented transform. Further, it
estimates the velocity information for each object to assist to the object
detection algorithm, effectively delineating multiple moving objects of
occlusion. The experimental results demonstrate that the proposed method can
correctly detect and track multiple moving objects with nonlinear motion
patterns and occlusions.
