Self-supervised classification of dynamic obstacles using the temporal information provided by videos
This work addresses the need for reducing labeled data requirements in autonomous driving systems, though it is incremental as it builds on existing self-supervised detection and segmentation methods.
The paper tackles the problem of classifying dynamic obstacles in autonomous driving by using self-supervised learning to analyze motion patterns from video sequences, achieving state-of-the-art performance on the BDD100K dataset.
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.