  This paper studies the interplay between kinematics (position and velocity)
and appearance cues for establishing correspondences in multi-target pedestrian
tracking. We investigate tracking-by-detection approaches based on a deep
learning detector, joint integrated probabilistic data association (JIPDA), and
appearance-based tracking of deep correspondence embeddings. We first addressed
the fixed-camera setup by fine-tuning a convolutional detector for accurate
pedestrian detection and combining it with kinematic-only JIPDA. The resulting
submission ranked first on the 3DMOT2015 benchmark. However, in sequences with
a moving camera and unknown ego-motion, we achieved the best results by
replacing kinematic cues with global nearest neighbor tracking of deep
correspondence embeddings. We trained the embeddings by fine-tuning features
from the second block of ResNet-18 using angular loss extended by a margin
term. We note that integrating deep correspondence embeddings directly in JIPDA
did not bring significant improvement. It appears that geometry of deep
correspondence embeddings for soft data association needs further investigation
in order to obtain the best from both worlds.
