@inproceedings {PUBA303,
	title = {Combining Multiple Classifiers and Context Information for Detecting Objects under Real-world Occlusion Patterns},
	author = {Marvin Struwe AND Stephan Hasler AND Ute Bauer-Wersing},
	year = {2013},
	month = {September},
	abstract = {Current state-of-the-art detection approaches reveal a strong degradation of performance with increasing occlusion of objects. In this paper we investigate different strategies to improve detection of occluded objects based on the analytic feature framework presented and compare the results in a car detection task. Motivated by an analysis of annotated traffic scenes we first describe a dedicated combination of classifiers to deal with the predominant car-car occlusion, and second, we propose a more general concept to handle vertical occlusion patterns. In
a final test, depth information is used as additional local cue to reason about visible object parts. We report first improvements and discuss advantages and drawbacks of the individual approaches to provide insight for further investigations.
},
	publisher = {NC2},
	booktitle = {New Challenges in Neural Computation (NC2) Workshop}
}