Temporal Coherence for Active Learning in Videos
This work addresses the data annotation bottleneck for autonomous driving, but it is incremental as it builds on existing active learning methods with a specific focus on video object detection.
The paper tackles the problem of expensive manual annotation for training autonomous driving systems by introducing a novel active learning approach for object detection in videos that exploits temporal coherence, showing it outperforms baselines on two datasets.
Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used to define the nodes of a graph and tracked forward and backward to temporally link the nodes. Minimizing an energy function defined on this graphical model provides estimates of both false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active learning for video object detection in road scenes. Finally, we show that our approach outperforms active learning baselines tested on two datasets.