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Real-time abandoned and stolen object detection based on spatio-temporal features in crowded scenes

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Abstract

Abandoned and stolen object detection is a challenging task due to occlusion, changes in lighting, large perspective distortion, and the similarity in appearance of different people. This paper presents real-time detection methods of abandoned and stolen objects in a complex video. The adaptive background modeling method is applied to stable tracking and the ghost image removing. To detect abandoned and stolen objects, the methods determine spatio-temporal relationship between moving people and suspicious drops. The space first detection method measures the distance between a moving object and a non-moving object in spatial change analysis. The time first detection method conducts temporal change analysis and then spatial change analysis. The potential abandoned object is classified as a definite abandoned or stolen object by two-level detection approach. The time-to-live timer is applied by adjusting several key parameters on each camera and environment. In experiments, we show the experimental results to evaluate our proposed methods using benchmark datasets.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Soonchunhyang University, Asan, 336-745, Korea

    Yunyoung Nam

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  1. Yunyoung Nam
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Correspondence to Yunyoung Nam.

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This research was supported by the Soonchunhyang University Research Fund (No. 20140226) and also was supported by the MSIP(Ministry of Science, ICT&Future Planning), Korea, under the C-ITRC(Convergence Information Technology Research Center) support program (NIPA-2014-H0401-14-1022) supervised by the NIPA(National IT Industry Promotion Agency).

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Nam, Y. Real-time abandoned and stolen object detection based on spatio-temporal features in crowded scenes. Multimed Tools Appl 75, 7003–7028 (2016). https://doi.org/10.1007/s11042-015-2625-2

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