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1 March 2010 Cascade of hierarchical context and appearance for object detection
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Conventional object-detection and localization approaches require extensive time to process the sliding background windows, which are not like the object at all. Global context of the subwindow gives access to alleviate such problems. In addition, many patch-based approaches often fail to search the patches at the correct locations and local context can help to resolve that. We propose an object-detection framework, which is top down and simple to implement. It combines global contextual features, local contextual features, and local appearance features in a coarse-to-fine cascade, which enables fast detection. The three features mentioned above play different roles in the process of object detection, and the representation with rich information makes detection robust and effective. The proposed approach shows satisfactory performance in both speed and accuracy.
©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)
Changxin Gao, Nong Sang, Jun Gao, and Qiling Tang "Cascade of hierarchical context and appearance for object detection," Optical Engineering 49(3), 037003 (1 March 2010).
Published: 1 March 2010

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