- Link:
- http://hdl.handle.net/1721.1/5566
- Collection:
-
- Subjects
- AI MIT Artificial Intelligence object detection pattern recognition people detection face detection car detection
- Creator:
- Papageorgiou, Constantine P.
- Format
- 128 p.
- Format
- 72537763 bytes
- Format
- 15910731 bytes
- Format
- application/postscript
- Format
- application/pdf
- Language
- en_US
- Relation
- AITR-1685
- Relation
- CBCL-186
- Description
- This thesis presents a general, trainable system
for object detection in static images and video sequences. The core
system finds a certain class of objects in static images of
completely unconstrained, cluttered scenes without using motion,
tracking, or handcrafted models and without making any assumptions
on the scene structure or the number of objects in the scene. The
system uses a set of training data of positive and negative example
images as input, transforms the pixel images to a Haar wavelet
representation, and uses a support vector machine classifier to
learn the difference between in-class and out-of-class patterns. To
detect objects in out-of-sample images, we do a brute force search
over all the subwindows in the image. This system is applied to
face, people, and car detection with excellent results. For our
extensions to video sequences, we augment the core static detection
system in several ways -- 1) extending the representation to five
frames, 2) implementing an approximation to a Kalman filter, and 3)
modeling detections in an image as a density and propagating this
density through time according to measured features. In addition,
we present a real-time version of the system that is currently
running in a DaimlerChrysler experimental vehicle. As part of this
thesis, we also present a system that, instead of detecting full
patterns, uses a component-based approach. We find it to be more
robust to occlusions, rotations in depth, and severe lighting
conditions for people detection than the full body version. We also
experiment with various other representations including pixels and
principal components and show results that quantify how the number
of features, color, and gray-level affect
performance.
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