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Moving object detection method based on complementary multi resolution background models 被引量:2
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作者 屠礼芬 仲思东 彭祺 《Journal of Central South University》 SCIE EI CAS 2014年第6期2306-2314,共9页
A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models ... A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences. 展开更多
关键词 moving object detection complementary Gaussian mixture models intermittent object motion thermal and dynamic background
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SMC-PHD based multi-target track-before-detect with nonstandard point observations model 被引量:5
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作者 占荣辉 高彦钊 +1 位作者 胡杰民 张军 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第1期232-240,共9页
Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method ... Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data. 展开更多
关键词 adaptive particle sampling multi-target track-before-detect probability hypothesis density(PHD) filter sequential Monte Carlo(SMC) method
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Maneuvering target track-before-detect via multiple-model Bernoulli particle filter
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作者 占荣辉 刘盛启 +1 位作者 胡杰民 张军 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第10期3935-3945,共11页
Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multi... Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo(particle) technique. Target detection was accomplished through the estimation of target's existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter. 展开更多
关键词 Bernoulli filter multiple model target maneuver track-before-detect(TBD) sequential Monte Carlo(SMC) technique
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基于深度卷积神经网络融合模型的路面裂缝识别方法 被引量:29
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作者 孙朝云 马志丹 +2 位作者 李伟 郝雪丽 申浩 《长安大学学报(自然科学版)》 EI CAS CSCD 北大核心 2020年第4期1-13,共13页
现有的路面裂缝识别方法大多仍局限于基于主动特征提取的处理技术,对路面图像来源有专一性要求,算法不具备泛化能力,现有的基于神经网络识别算法对设备有特定要求,且路面裂缝的定位准确性不高。为此,提出基于深度卷积神经网络融合模型... 现有的路面裂缝识别方法大多仍局限于基于主动特征提取的处理技术,对路面图像来源有专一性要求,算法不具备泛化能力,现有的基于神经网络识别算法对设备有特定要求,且路面裂缝的定位准确性不高。为此,提出基于深度卷积神经网络融合模型的路面裂缝识别方法。首先,应用多目标SSD卷积神经网络模型对路面裂缝进行分类检测,然后使用深度残差网络对SSD模型的特征提取结构进行改进,并根据损失函数的收敛程度对模型中的超参数进行优化,提高路面裂缝分类和定位的准确率;其次,针对裂缝分类检测模型对路面裂缝定位存在的偏差,提出基于U-Net模型的路面裂缝分割方法,并改进模型的特征提取网络,提高裂缝分割精度,实现精确的裂缝分割;最后,将裂缝分类检测模型与分割模型进行融合,加载2个模型并导入上述训练得到最优权重,根据裂缝分类网络判断路面图像有无裂缝,若存在裂缝则给出具体类别和置信度,并将这些信息和原始裂缝图像输入U-Net分割网络,根据分割结果计算线性裂缝的长度、宽度及网状裂缝的面积。试验结果表明:给出的路面裂缝识别方法对于横向裂缝、纵向裂缝和网状裂缝的识别精度分别为86.6%、87.2%和85.3%;该方法不仅能够给出路面裂缝的类别信息,还可以给出路面裂缝的精确定位和几何参数信息,可直接用于路面状况评价。 展开更多
关键词 道路工程 路面裂缝识别 深度卷积神经网络 多目标检测模型 裂缝分割 模型融合
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