High-Accuracy Scene Recognition and Its Application to Highly-Safe Intelligent Systems
Kenichi Takada, Michitaka Kameyama · 2018
It is expected that a highly-safe intelligent system which makes risk avoidance in the real-world environment where human lives. In order to detect dangerous situations, it is indispensable to accurately recognize risk events from the input image information, i.e., high-accuracy scene recognition. In this paper, it is clarified that the high-accuracy scene recognition can be achieved by combination of the convolution neural network which extracts the feature of an input image and the support vector machine for classification. In the scene recognition of a single danger event, it is not possible to recognize multiple event occurrences (multi-class classification). We propose a method to realize multi-class classification without decreasing the recognition accuracy by selectively using an individual classifier generated for each event.