Abstract
Moving car discovery is one of the most essential problems in image processing. It is a very challenging problem that attracts many attentions recently. Major part of previous moving car discovery methods engages radar signals. Nevertheless, those face some troubles in special cases, for example they have difficulty in detection of moving cars in zigzag movements. Machine learning methods can be utilized to conquer these inefficiencies. For online moving car discovery, we propose to employ hierarchical partitioning over the features extracted from image. Each moving car is corresponds to a partition. Unlike the traditional partitioning algorithms, the threshold distance in the proposed method is not fixed. This threshold value is tuned by a Gaussian distribution. Harris features are applied to capture the corner features. Experimentations show the proposed method outperforms other competent methods.
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Azimifar, M., Rad, F., Parvin, H. (2015). Online Detection of Moving Object in Video. In: Bikakis, A., Zheng, X. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2015. Lecture Notes in Computer Science(), vol 9426. Springer, Cham. https://doi.org/10.1007/978-3-319-26181-2_26
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DOI: https://doi.org/10.1007/978-3-319-26181-2_26
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