Learning object detectors requires massive amounts of labeled training
samples from the specific data source of interest. This is impractical when
dealing with many different sources (e.g., in camera networks), or constantly
changing ones such as mobile cameras (e.g., in robotics or driving assistant
systems). In this paper, we address the problem of self-learning detectors in
an autonomous manner, i.e. (i) detectors continuously updating themselves to
efficiently adapt to streaming data sources (contrary to transductive
algorithms), (ii) without any labeled data strongly related to the target data
stream (contrary to self-paced learning), and (iii) without manual intervention
to set and update hyper-parameters. To that end, we propose an unsupervised,
on-line, and self-tuning learning algorithm to optimize a multi-task learning
convex objective. Our method uses confident but laconic oracles (high-precision
but low-recall off-the-shelf generic detectors), and exploits the structure of
the problem to jointly learn on-line an ensemble of instance-level trackers,
from which we derive an adapted category-level object detector. Our approach is
validated on real-world publicly available video object datasets.

本文提出了一种适用于移动摄像头、自适应于不同数据源的无监督多任务自学习算法，通过结合性地学习实例级追踪器，由此获得调整后的类别级物体检测器。