Object Detection in Images by Components
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In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is described here for people is easily applied to other objects as well. The motivation for developing a component based approach is two fold: first, to enhance the performance of person detection systems on frontal and rear views of people and second, to develop a framework that directly addresses the problem of detecting people who are partially occluded or whose body parts blend in with the background. The data classification is handled by several support vector machine classifiers arranged in two layers. This architecture is known as Adaptive Combination of Classifiers (ACC). The system performs very well and is capable of detecting people even when all components of a person are not found. The performance of the system is significantly better than a full body person detector designed along similar lines. This suggests that the improved performance is due to the components based approach and the ACC data classification structure.
AI, MIT, Artificial Intelligence, Object Detection, sComputer Vision, Pattern Recognition, Detection bysComponents, Machine Learning, People Detection, sWavelets, Detection of Partially Occluded Objects, sViewpoint Invariant Detection, Example Based Learnin
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Papageorgiou, Constantine P.; Poggio, Tomaso (1999-10-13)This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a ...