Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
This addresses the problem of robust object tracking in-the-wild for surveillance applications, offering a novel method that improves over existing appearance-based trackers.
The paper tackles object tracking in challenging real-world conditions by leveraging human-annotated video libraries through a search and retrieval approach, achieving state-of-the-art performance on surveillance datasets and introducing a new dataset for complex appearance changes.
Humans use context and scene knowledge to easily localize moving objects in conditions of complex illumination changes, scene clutter and occlusions. In this paper, we present a method to leverage human knowledge in the form of annotated video libraries in a novel search and retrieval based setting to track objects in unseen video sequences. For every video sequence, a document that represents motion information is generated. Documents of the unseen video are queried against the library at multiple scales to find videos with similar motion characteristics. This provides us with coarse localization of objects in the unseen video. We further adapt these retrieved object locations to the new video using an efficient warping scheme. The proposed method is validated on in-the-wild video surveillance datasets where we outperform state-of-the-art appearance-based trackers. We also introduce a new challenging dataset with complex object appearance changes.