Learning to Detect Vehicles by Clustering Appearance Patterns
This work addresses vehicle detection for applications like autonomous driving, but it is incremental as it builds on existing AdaBoost and clustering methods.
The paper tackles intra-category diversity in object detection by learning an ensemble of models from visual and geometrical clusters of vehicle instances, resulting in improved detection performance and orientation estimation accuracy.
This paper studies efficient means for dealing with intra-category 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 employed with pixel lookup features for fast detection. The analysis provides insight into the design of a robust vehicle detection system, showing promise in terms of detection performance and orientation estimation accuracy.