Robust vehicle detection in low-resolution aerial imagery

Samir Sahli; Yueh Ouyang; Yunlong Sheng; Daniel A. Lavigne

doi:10.1117/12.850387

26 April 2010 Robust vehicle detection in low-resolution aerial imagery

Samir Sahli, Yueh Ouyang, Yunlong Sheng, Daniel A. Lavigne

Proceedings Volume 7668, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VII; 76680G (2010) https://doi.org/10.1117/12.850387
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

We propose a feature-based approach for vehicle detection in aerial imagery with 11.2 cm/pixel resolution. The approach is free of all constraints related to the vehicles appearance. The scale-invariant feature transform (SIFT) is used to extract keypoints in the image. The local structure in the neighbouring of the SIFT keypoints is described by 128 gradient orientation based features. A Support Vector Machine is used to create a model which is able to predict if the SIFT keypoints belong to or not to car structures in the image. The collection of SIFT keypoints with car label are clustered in the geometric space into subsets and each subset is associated to one car. This clustering is based on the Affinity Propagation algorithm modified to take into account specific spatial constraint related to geometry of cars at the given resolution.

Citation Download Citation

Samir Sahli, Yueh Ouyang, Yunlong Sheng, and Daniel A. Lavigne "Robust vehicle detection in low-resolution aerial imagery", Proc. SPIE 7668, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VII, 76680G (26 April 2010); https://doi.org/10.1117/12.850387

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