Name
Title | ||
|---|---|---|
Searching Action Proposals Via Spatial Actionness Estimation And Temporal Path Inference And Tracking |
Abstract | ||
|---|---|---|
In this paper, we address the problem of searching action proposals in unconstrained video clips. Our approach starts from actionness estimation on frame-level bounding boxes, and then aggregates the bounding boxes belonging to the same actor across frames via linking, associating, tracking to generate spatial-temporal continuous action paths. To achieve the target, a novel actionness estimation method is firstly proposed by utilizing both human appearance and motion cues. Then, the association of the action paths is formulated as a maximum set coverage problem with the results of actionness estimation as a priori. To further promote the performance, we design an improved optimization objective for the problem and provide a greedy search algorithm to solve it. Finally, a tracking-by-detection scheme is designed to further refine the searched action paths. Extensive experiments on two challenging datasets, UCF-Sports and UCF-101, show that the proposed approach advances state-of-the-art proposal generation performance in terms of both accuracy and proposal quantity. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-54184-6_24 | COMPUTER VISION - ACCV 2016, PT II |
DocType | Volume | ISSN |
Conference | 10112 | 0302-9743 |
ISBN | Citations | PageRank |
9783319541839 | 3 | 0.37 |
References | Authors | |
24 | 5 | |