This paper addresses the problem of object discovery from unlabeled driving videos captured in a realistic automotive setting.
We propose to leverage a generic object tracker in order to perform object mining in large-scale unlabeled videos, captured in a realistic automotive setting.
In the past decade many robots were deployed in the wild, and people detection and tracking is an important component of such deployments.
We explore object discovery and detector adaptation based on unlabeled video sequences captured from a mobile platform.
With the rise of end-to-end learning through deep learning, person detectors and re-identification (ReID) models have recently become very strong.