Deep multiple classifier fusion for traffic scene recognition

The recognition of the traffic scene in still images is an important yet difficult task in an intelligent transportation systems. The main difficulty lies in how to improve the image processing algorithms for different traffic participants and the various layouts of roads while discriminating the different traffic scenes. In this paper, we attempt to solve the traffic scene recognition problem with three distinct contributions. First, we propose a deep multi-classifier fusion method in the setting of granular computing. Specifically, the deep multi-classifier fusion method which involves local deep-learned feature extraction at one end that is connected to the other end for classification through a multi-classifier fusion approach. At the local deep-learned feature extraction end, the operation of convolution to extract feature maps from the local patches of an image is essentially a form of information granulation, whereas the fusion of classifiers at the classification end is essentially a form of organization. The second contribution is the creation of new traffic scene data set, named the “WZ-traffic”. The WZ-traffic data set consists of 6035 labeled images, which belong to 20 categories collected from both an image search engine as well as from personal photographs. Third, we make extensive comparisons with state-of-the-art methods on the WZ-traffic and FM2 data sets. The experiment results demonstrate that our method dramatically improves traffic scene recognition and brings potential benefits to many other real-world applications.