In this paper, our proposed framework takes a remarkably different direction to resolve multi-view detection problem in a bottom-up fashion. Firstly, a state of the art 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, where the human labeled training data or a generic detector are not needed. Secondly, this learning process is conveniently replicated many times in different surveillance scenes and result in a particular detector under various camera viewpoints. Thus, the proposed framework can be employed in multi-view object detection application with unsupervised manner. Obviously, the initial scene-specific detector, initialed by several bounding boxes, exhibits poor detection performance and difficult to improve by traditional online learning algorithm. Consequently, in our self-learning process, we propose to partition detection responses space by online hybrid classifiers and assign each partition individual classifier that achieves the high classification accuracy progressively. A novel online gradual learning algorithm is proposed to train the hybrid classifiers 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. Experimental results on several video datasets show that our approach achieves comparable performance to robust supervised methods and outperforms the state of the art online learning methods in varying imaging conditions.
['Dapeng Luo', 'Zhipeng Zeng', 'Longsheng Wei', 'Yongwen Liu', 'Luo Cf', 'Jun Chen', 'Nong Sang']
Autonomous Learning Framework Based on Online Hybrid Classifier for Multi-view Object Detection in Video