A vision based traffic accident detection method using extreme learning machine

Abstract

Over the past years, automatic traffic accident detection (ATAD) based on video has become one of the most promising applications in intelligent transportation and is playing a more and more important role in ensuring travel safety. This paper proposes a classifier-based supervised method by viewing the last seconds before motor vehicle collisions as the detection target. In our method, we devise a novel algorithm called OF-SIFT as the low-level feature. Deriving from the optical flow and Scale Invariant Feature Transform (SIFT), it is designed to extract local motion information from the temporal domain rather than gradient-based local appearance from the spatial domain. The purpose of OF-SIFT is to generate a feature that can capture sufficient and distinctive dynamic motion information for motion detection without using the static state information of moving objects. Further, in order to develop a more compact image representation without considering the explicit vehicle geometry shape, we use the idea of Bag of Feature (BOF) model with spatial information to encode features. Finally, an extreme learning machine (ELM) classifier is introduced as the basic classifier owing to its excellent and fast generalization. Experiments using real-world data have shown that the proposed method has achieved good performance in handling ordinary video scenes.