  This paper presents a novel approach to improve the accuracy of tracking
multiple objects in a static scene using a particle filter system by
introducing a data association step, a state queue for the collection of
tracked objects and adaptive parameters to the system. The data association
step makes use of the object detection phase and appearance model to determine
if the approximated targets given by the particle filter step match the given
set of detected objects. The remaining detected objects are used as information
to instantiate new objects for tracking. State queues are also used for each
tracked object to deal with occlusion events and occlusion recovery. Finally we
present how the parameters adjust to occlusion events. The adaptive property of
the system is also used for possible occlusion recovery. Results of the system
are then compared to a ground truth data set for performance evaluation. Our
system produced accurate results and was able to handle partially occluded
objects as well as proper occlusion recovery from tracking multiple objects
