  In this paper, we propose a multi-object detection and tracking method using
depth cameras. Depth maps are very noisy and obscure in object detection. We
first propose a region-based method to suppress high magnitude noise which
cannot be filtered using spatial filters. Second, the proposed method detect
Region of Interests by temporal learning which are then tracked using weighted
graph-based approach. We demonstrate the performance of the proposed method on
standard depth camera datasets with and without object occlusions. Experimental
results show that the proposed method is able to suppress high magnitude noise
in depth maps and detect/track the objects (with and without occlusion).
