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Recognizing Anomalies in Urban Road Scenes Through Analysing Single Images Captured by Cameras on Vehicles

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Abstract

In this paper, we propose to recognize anomalies in urban road scenes through analysing single images captured by cameras on driving vehicles. Anomaly detection is one of the most important functions for visual driver-assistance systems and autonomous vehicles. Anomaly detection provides drivers and autonomous vehicles important information about driving environments and help them drive more safely. In this work, we define anything on roads that are within a certain distance of a driving vehicle and pose potential dangers for it as anomalies, such as traffic accidents, reckless driven vehicles and pedestrians. The proposed approach recognizes anomalies in urban road scenes by analysing appearances of single images captured by cameras on driving vehicles. To do so, first, we collect a large number of urban road scene images that do not contain any anomalies. Second, we segment the road regions from these images and represent the obtained road regions based on the bag of visual words method. After that, we apply k-means clustering to the region representations for acquiring a small set of reference images. Third, we establish dense correspondence between input images and the reference images to create representations for the input images. Following that, representations of normal images are used to train a one-class Support Vector Machine classifier. Finally, we use the classifier to recognize images containing anomalies. Experiments on urban road scene images are conducted. Obtained results demonstrate that by using the proposed approach we can recognize urban road scene images containing anomalies.

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Bei**g Jiaotong University, No. 3 Shang Yuan Cun, Hai Dian District, Bei**g, China

    Shuang Bai

  2. School of Electronic and Information Engineering, Beihang University, No. 37 Xue Yuan Lu, Hai Dian District, Bei**g, China

    Chao Han

  3. Bei**g **gdong Shangke Information Technology Co., Ltd, Bei**g, China

    Shan An

Authors
  1. Shuang Bai
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  2. Chao Han
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  3. Shan An
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Correspondence to Shuang Bai.

Additional information

This work was Supported by National Natural Science Foundation of China (61602027).

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Bai, S., Han, C. & An, S. Recognizing Anomalies in Urban Road Scenes Through Analysing Single Images Captured by Cameras on Vehicles. Sens Imaging 19, 34 (2018). https://doi.org/10.1007/s11220-018-0218-7

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