Performance analysis and learning approaches for vehicle detection and counting in aerial images
Vasu Parameswaran, Philippe Burlina, Rama Chellappa · 2002
Robustness as well as the ability to work in an unsupervised mode are two desirable features of algorithms employed on large image databases. This paper describes parameter optimization strategies for such algorithms and motivates these strategies by focussing on aerial image exploitation and studying certain specific aerial image understanding algorithms, namely local vehicle detection and global vehicle configuration detection. The paper first gives a brief introduction to the problem in the context of aerial imagery. Next, a high level description of the algorithms and parameters that need to be optimized is given. Strategies for parameter optimization are illustrated using examples. Finally a discussion on the applicability and scope for improvement of the strategies is given.