复杂场景中的运动目标检测研究
首发时间:2017-05-03
摘要:目前,虽然已有许多关于运动目标检测技术被提出并应用,但受现实场景及技术本身复杂性的限制,在技术和实践方面仍然存在一些弱点和问题需要被改进和解决,如检测算法的鲁棒性和实时性。本文基于前人的研究提出了一种新的自适应背景差分法。该方法根据样本一致性和样本差异性统计模版先对图像进行初始分割,再结合错误检测抑制模型消除一些误检区域,最后利用基于区域边界评价的确定性前景更新和概率性更新策略实现背景模型的更新。实验表明本文的检测方法能够实现复杂场景的检测,相比于大部分检测算法,本文算法具有较好的鲁棒性,同时满足实时检测的要求。
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Research on Moving Target Detection in Complcated Environment
Abstract:Although a large quantity of technologies in moving targets detection domain are proposed, there are some difficulties and challenges without good solutions, especially in complex environment. In general, how to improve simultaneously the robust and efficiency of detection is most severe challenge.Therefore, we proposes a newly adaptive background subtraction for moving objects detection.The proposed background subtraction is consisted of three parts: first, we build a sample disparity and a sample consensus statistical mask in the initial segmentation phase. Second, adding a false positives suppression model into the detection method. In addition, it exploits a new update scheme by integrating probabilistic and certain manner. We use the 2012 Change Detection dataset to evaluate the performance of the proposed background subtraction in the experiment. Results demonstrate that the proposed method can suitable for the most complex environment and outperforms the most existing detection methods.
Keywords: image processing background subtraction target detection
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