Title :
Learning to Detect Vehicles by Clustering Appearance Patterns
Author :
Ohn-Bar, Eshed ; Trivedi, Mohan Manubhai
Author_Institution :
Lab. for Intell. & Safe Automobiles, Univ. of California, San Diego, La Jolla, CA, USA
Abstract :
This paper studies efficient means in dealing with 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 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.
Keywords :
feature extraction; learning (artificial intelligence); object detection; pattern clustering; road vehicles; AdaBoost detection scheme; appearance pattern clustering; ensemble learning; object detection; occlusion handling; orientation handling; pixel lookup feature detection; vehicle detection system; Detectors; Feature extraction; Image color analysis; Support vector machines; Three-dimensional displays; Vehicles; Visualization; Active safety; mining appearance patterns; multiorientation detection; object detection; occlusion-handling; orientation estimation; vehicle detection;
Journal_Title :
Intelligent Transportation Systems, IEEE Transactions on
DOI :
10.1109/TITS.2015.2409889