  Nowadays, autonomous driving systems can detect, segment, and classify the
surrounding obstacles using a monocular camera. However, state-of-the-art
methods solving these tasks generally perform a fully supervised learning
process and require a large amount of training labeled data. On another note,
some self-supervised learning approaches can deal with detection and
segmentation of dynamic obstacles using the temporal information available in
video sequences. In this work, we propose to classify the detected obstacles
depending on their motion pattern. We present a novel self-supervised framework
consisting of learning offline clusters from temporal patch sequences and
considering these clusters as labeled sets to train a real-time image
classifier. The presented model outperforms state-of-the-art unsupervised image
classification methods on large-scale diverse driving video dataset BDD100K.
