  Multiple Object Tracking (MOT) is a long-standing task in computer vision.
Current approaches based on the tracking by detection paradigm either require
some sort of domain knowledge or supervision to associate data correctly into
tracks. In this work, we present an unsupervised multiple object tracking
approach based on visual features and minimum cost lifted multicuts. Our method
is based on straight-forward spatio-temporal cues that can be extracted from
neighboring frames in an image sequences without superivison. Clustering based
on these cues enables us to learn the required appearance invariances for the
tracking task at hand and train an autoencoder to generate suitable latent
representation. Thus, the resulting latent representations can serve as robust
appearance cues for tracking even over large temporal distances where no
reliable spatio-temporal features could be extracted. We show that, despite
being trained without using the provided annotations, our model provides
competitive results on the challenging MOT Benchmark for pedestrian tracking.
