
Proceedings Paper
Low-resolution vehicle tracking using dense and reduced local gradient features mapsFormat | Member Price | Non-Member Price |
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Paper Abstract
We present a novel method to quickly detect and track objects of low resolution within an image frame by comparing
dense, oriented gradient features at multiple scales within an object chip. The proposed method uses vector correlation
between sets of oriented Haar filter responses from within a local window and an object library to create similarity
measures, where peaks indicate high object probability. Interest points are chosen based on object shape and size so that
each point represents both a distinct spatial location and the shape segment of the object. Each interest point is then
independently searched in subsequent frames, where multiple similarity maps are fused to create a single object
probability map. This method executes in real time by reducing feature calculations and approximations using box
filters and integral images. We achieve invariance to rotation and illumination, because we calculate interest point
orientation and normalize the feature vector scale. The method creates a feature set from a small and localized area,
allowing for accurate detections in low resolution scenarios. This approach can also be extended to include the detection
of partially occluded objects through calculating individual interest point feature vector correlations and clustering points
together. We have tested the method on a subset of the Columbus Large Image Format (CLIF) 2007 dataset, which
provides various low-pixel-on-object moving and stationary vehicles with varying operating conditions. This method
provides accurate results with minimal parameter tuning for robust implementation on aerial, low pixel-on-object data
sets for automated classification applications.
Paper Details
Date Published: 7 May 2010
PDF: 8 pages
Proc. SPIE 7694, Ground/Air Multi-Sensor Interoperability, Integration, and Networking for Persistent ISR, 76941I (7 May 2010); doi: 10.1117/12.853273
Published in SPIE Proceedings Vol. 7694:
Ground/Air Multi-Sensor Interoperability, Integration, and Networking for Persistent ISR
Michael A. Kolodny, Editor(s)
PDF: 8 pages
Proc. SPIE 7694, Ground/Air Multi-Sensor Interoperability, Integration, and Networking for Persistent ISR, 76941I (7 May 2010); doi: 10.1117/12.853273
Show Author Affiliations
Michael P. Dessauer, Louisiana Tech Univ. (United States)
Sumeet Dua, Louisiana Tech Univ. (United States)
Published in SPIE Proceedings Vol. 7694:
Ground/Air Multi-Sensor Interoperability, Integration, and Networking for Persistent ISR
Michael A. Kolodny, Editor(s)
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