Component-based car detection in street scene images
Name
57015536-MIT.pdf
Description
Full printable version
Size
6.29 MB
Format
Adobe PDF
Checksum (MD5)
c08a980f77df772c4463f836d587325b
Author(s)
Leung, Brian, 1981-
Advisor(s)
Tomaso Poggio.
Date Issued
2004
Publisher
Massachusetts Institute of Technology
Abstract
Recent studies in object detection have shown that a component-based approach is more resilient to partial occlusions of objects, and more robust to natural pose variations, than the traditional global holistic approach. In this thesis, we consider the task of building a component-based detector in a more difficult domain: cars in natural images of street scenes. We demonstrate reasonable results for two different component-based systems, despite the large inherent variability of cars in these scenes. First, we present a car classification scheme based on learning similarities to features extracted by an interest operator. We then compare this system to traditional global approaches that use Support Vector Machines (SVMs). Finally, we present the design and implementation of a system to locate cars based on the detections of human-specified components.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 67-71).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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