What is damaged: a benchmark dataset for abnormal traffic object classification

Traffic-related multimedia analysis has become increasingly important in both research community and industry. In this paper, we study the problem of image-based classification of abnormal traffic objects. Different from previous works that focusing on only the normal object categories, our work aims to classify both the category and the working status of a traffic object. We construct a new dataset, namely Abnormal Traffic Object Classification (ATOC), for the study of the above problem. ATOC contains 6 kinds of traffic objects and for each main category there are also two sub-categories covering the normal and abnormal status of the objects. We propose a novel deep-learning based framework to solve our problem and provide a strong baseline for future studies. Specifically, we adopt a pre-trained deep convolutional network for feature extraction and use support vector machine for classification. We also utilize random sample pairing to augment the dataset and introduce attention mechanism to further refine the feature representation. Experimental results demonstrate that the proposed method achieves superior performance than the state-of-art deep learning approaches for the recognition of objects’ categories and the corresponding working status in traffic scenarios.