Local object information, such as the appearance and motion features of the object, are useful for object tracking in videos provided the object is not occluded by other elements in the scene. During occlusion, however, the local object information in the video frame does not properly represent the true properties of the object, which leads to tracking failure. We propose a framework that combines multiple cues including the local object information, the background characteristics and group motion dynamics to improve object tracking in challenging cluttered environments. The performance of the proposed tracking model is compared with the kernelised correlation filter (KCF) tracker. In the tested video sequences the proposed tracking model correctly tracked objects even when the KCF tracker failed because of occlusion and background noise.