PROJECT TITLE :

Learning to Detect Vehicles by Clustering Appearance Patterns

ABSTRACT:

This paper studies efficient suggests that in handling intracategory diversity in object detection. Strategies for occlusion and orientation handling are explored by learning an ensemble of detection models from visual and geometrical clusters of object instances. An AdaBoost detection scheme is used with pixel lookup features for quick detection. The analysis provides insight into the planning of a strong vehicle detection system, showing promise in terms of detection performance and orientation estimation accuracy.


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