Online Generative-Discriminative Model for Object Detection in Video: An Unsupervised Learning Framework
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Traditional single-view object detection methods often perform worse under unconstrained video environments. To address this problem, many modern multi-view detection approaches model complex 3D appearance representations to predict the optimal viewing angle for detection. Most of these approaches require an intensive training process on large database, collected in advance. In this paper, the proposed framework takes a remarkably different direction to resolve multi-view detection problem in a bottom-up fashion. First, a scene-specific objector is obtained from a fully autonomous learning process triggered by marking several bounding boxes around the object in the first video frame via a mouse. Here the human labeled training data or a generic detector are not needed. Second, this learning process is conveniently replicated many times in different surveillance scenes and results in a particular detector under various camera viewpoints. Thus, the proposed framework can be employed in multi-view object detection applications from unsupervised learning process. Obviously, the initial scene-specific detector, initialed by several bounding boxes, exhibits poor detection performance and is difficult to improve with traditional online learning algorithm. Consequently, we propose Generative-Discriminative model to partition detection response space and assign each partition an individual descriptor that progressively achieves high classification accuracy. A novel online gradual learning algorithm is proposed to train the Generative-Discriminative model automatically and focus online learning on the hard samples: the most informative samples lying around the decision boundary. The output is a hybrid classifier based scene-specific detector which achieves decent performance under different viewing angles.
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