Paper Info
Title
Persistent Multiple Hypothesis Tracking For Wide Area Motion Imagery
Abstract
Wide area motion imagery (WAMI) acquired by an airborne sensor enables continuous monitoring of large urban areas. Reliable vehicle tracking in this imagery remains challenging due to low frame rate and small object size. Many approaches solely rely on motion detections provided by frame differencing or background subtraction. Recent approaches for persistent tracking, i.e. tracking vehicles even if they become stationary, compensate for missing motion detections by combining a detection-based tracker with a second tracker based on appearance or local context. We propose a novel single tracker framework based on multiple hypothesis tracking (MHT) that enables persistent tracking in WAMI data by recovering missing motion detections with a classifier-based detector, thus avoiding the additional complexity introduced by combining two trackers. We adapt the MHT approach to the specific context of WAMI tracking by integrating an appearance-based similarity measure, vehicle-collision tests, and clutter handling. An evaluation on a region of interest in the WPAFB 2009 dataset shows state-of-the-art performance.
Year
Venue
Keywords
2017
2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
multi-target tracking, wide area aerial surveillance, wide area motion imagery
Field
DocType
ISSN
Background subtraction,Computer vision,BitTorrent tracker,Histogram,Motion detection,Similarity measure,Pattern recognition,Computer science,Clutter,Frame rate,Artificial intelligence,Vehicle tracking system
Conference
1522-4880
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Raphael Spraul100.34
Christine Hartung200.34
Tobias Schuchert39312.21