  Tracking multiple objects in real time is essential for a variety of
real-world applications, with self-driving industry being at the foremost. This
work involves exploiting temporally varying appearance and motion information
for tracking. Siamese networks have recently become highly successful at
appearance based single object tracking and Recurrent Neural Networks have
started dominating both motion and appearance based tracking. Our work focuses
on combining Siamese networks and RNNs to exploit appearance and motion
information respectively to build a joint system capable of real time
multi-object tracking. We further explore heuristics based constraints for
tracking in the Birds Eye View Space for efficiently exploiting 3D information
as a constrained optimization problem for track prediction.
