# Active Learning in Video Tracking

arXiv: https://arxiv.org/abs/1912.12557 · HTML: https://mlsanity.com/paper/1912.12557/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [Correlation-aware active learning for surgery video segmentation](https://mlsanity.com/paper/2311.08811/) (2023)
- [Active Learning for Video Classification with Frame Level Queries](https://mlsanity.com/paper/2307.05587/) (2023)

## Abstract (arXiv)

Active learning methods, like uncertainty sampling, combined with probabilistic prediction techniques have achieved success in various problems like image classification and text classification. For more complex multivariate prediction tasks, the relationships between labels play an important role in designing structured classifiers with better performance. However, computational time complexity limits prevalent probabilistic methods from effectively supporting active learning. Specifically, while non-probabilistic methods based on structured support vector machines can be tractably applied to predicting bipartite matchings, conditional random fields are intractable for these structures. We propose an adversarial approach for active learning with structured prediction domains that is tractable for matching. We evaluate this approach algorithmically in an important structured prediction problems: object tracking in videos. We demonstrate better accuracy and computational efficiency for our proposed method.