  We present a data association method for vision-based multiple pedestrian
tracking, using deep convolutional features to distinguish between different
people based on their appearances. These re-identification (re-ID) features are
learned such that they are invariant to transformations such as rotation,
translation, and changes in the background, allowing consistent identification
of a pedestrian moving through a scene. We incorporate re-ID features into a
general data association likelihood model for multiple person tracking,
experimentally validate this model by using it to perform tracking in two
evaluation video sequences, and examine the performance improvements gained as
compared to several baseline approaches. Our results demonstrate that using
deep person re-ID for data association greatly improves tracking robustness to
challenges such as occlusions and path crossings.
