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Multiple Object Tracking (MOT) is a long-standing task in computer vision.Current approaches based on the tracking by detection paradigm either requiresome sort of domain knowledge or supervision to associate data correctly intotracks. In this work, we present an unsupervised multiple object trackingapproach based on visual features and minimum cost lifted multicuts. Our methodis based on straight-forward spatio-temporal cues that can be extracted fromneighboring frames in an image sequences without superivison. Clustering basedon these cues enables us to learn the required appearance invariances for thetracking task at hand and train an autoencoder to generate suitable latentrepresentation. Thus, the resulting latent representations can serve as robustappearance cues for tracking even over large temporal distances where noreliable spatio-temporal features could be extracted. We show that, despitebeing trained without using the provided annotations, our model providescompetitive results on the challenging MOT Benchmark for pedestrian tracking.

Unsupervised Multiple Person Tracking using AutoEncoder-Based Lifted Multicuts