Boosting, AdaBoost, Viola-Jones Detector
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00:00:00
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Lecture 11: Sliding-Window based Object Detection II |
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00:00:24
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Course Outline |
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00:01:12
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Topics of This Lecture |
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00:01:50
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Recap: Sliding-Window Object Detection |
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00:04:40
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Recap: Support Vector Machines (SVM) |
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00:13:56
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Recap: Non-Linear SVMs |
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00:16:56
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Recap: Gradient-based Representations |
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00:18:41
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Recap: HOG Descriptor Processing Chain |
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00:21:33
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Recap: Pedestrian Detection with HOG |
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00:22:27
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Recap: Non-Maximum Suppression |
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00:23:13
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Applications: Mobile Robot Navigation |
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00:31:24
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Classifier Construction: Many Choices... |
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00:31:40
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Boosting |
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00:33:48
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AdaBoost: Intuition |
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00:36:31
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AdaBoost - Formalization |
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00:38:51
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AdaBoost: Detailed Training Algorithm |
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00:44:00
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AdaBoost: Recognition |
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00:47:08
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Example: Face Detection |
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00:48:49
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Feature extraction |
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00:54:29
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Example |
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00:55:36
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Large Library of Filters |
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00:58:34
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AdaBoost for Feature+Classifier Selection |
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01:03:46
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AdaBoost for Efficient Feature Selection |
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01:05:24
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Cascading Classifiers for Detection |
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01:09:28
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Cascading Classifiers |
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01:11:04
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Viola-Jones Face Detector: Summary |
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01:13:11
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Cascading Classifiers |
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01:14:45
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Viola-Jones Face Detector: Summary |
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01:18:02
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Practical Issue: Bootstrapping |
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01:20:43
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Viola-Jones Face Detector: Results |
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01:22:21
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You Can Try It At Home... |
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01:23:05
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Example Application |
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01:25:01
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Summary: Sliding-Windows |
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01:26:44
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Feature Computation Trade-Off |
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01:28:22
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What Slows Down HOG (CUDA Implem.) |
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01:29:30
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References and Further Reading |