This paper presents a hybrid 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 are adaptively adjusted 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. Applying the system to a limited dataset, it produces quite accurate results and was able to handle partially occluded objects as well as proper occlusion recovery from tracking multiple objects. We also present a comparison of this method compared to an ordinary particle filter. Results show that although it may improve the accuracy in terms of correcting the system after occlusion, it works on a case to case basis.