  Multi-object tracking has recently become an important area of computer
vision, especially for Advanced Driver Assistance Systems (ADAS). Despite
growing attention, achieving high performance tracking is still challenging,
with state-of-the- art systems resulting in high complexity with a large number
of hyper parameters. In this paper, we focus on reducing overall system
complexity and the number hyper parameters that need to be tuned to a specific
environment. We introduce a novel tracking system based on similarity mapping
by Enhanced Siamese Neural Network (ESNN), which accounts for both appearance
and geometric information, and is trainable end-to-end. Our system achieves
competitive performance in both speed and accuracy on MOT16 challenge, compared
to known state-of-the-art methods.
