DocumentCode :
3672545
Title :
Joint inference of groups, events and human roles in aerial videos
Author :
Tianmin Shu;Dan Xie;Brandon Rothrock;Sinisa Todorovic;Song-Chun Zhu
Author_Institution :
Center for Vision, Cognition, Learning and Art, University of California, Los Angeles, USA
fYear :
2015
fDate :
6/1/2015 12:00:00 AM
Firstpage :
4576
Lastpage :
4584
Abstract :
With the advent of drones, aerial video analysis becomes increasingly important; yet, it has received scant attention in the literature. This paper addresses a new problem of parsing low-resolution aerial videos of large spatial areas, in terms of 1) grouping, 2) recognizing events and 3) assigning roles to people engaged in events. We propose a novel framework aimed at conducting joint inference of the above tasks, as reasoning about each in isolation typically fails in our setting. Given noisy tracklets of people and detections of large objects and scene surfaces (e.g., building, grass), we use a spatiotemporal AND-OR graph to drive our joint inference, using Markov Chain Monte Carlo and dynamic programming. We also introduce a new formalism of spatiotemporal templates characterizing latent sub-events. For evaluation, we have collected and released a new aerial videos dataset using a hex-rotor flying over picnic areas rich with group events. Our results demonstrate that we successfully address above inference tasks under challenging conditions.
Keywords :
Lead
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2015.7299088
Filename :
7299088
Link To Document :
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