This work presents an occlusion aware hand tracker to reliably track both hands of a person using a monocular RGB camera. To demonstrate its robustness, we evaluate the tracker on a challenging, occlusion-ridden naturalistic driving dataset, where hand motions of a driver are to be captured reliably. The proposed framework additionally encodes and learns tracklets corresponding to complex (yet frequently occurring) hand interactions offline, and makes an informed choice during data association. This provides positional information of the left and right hands with no intrusion (through complete or partial occlusions) over long, unconstrained video sequences in an online manner. The tracks thus obtained may find use in domains such as human activity analysis, gesture recognition, and higher-level semantic categorization.