Single-feature methods are unable to effectively track a target in an underground coal mine video due to the high background noise, low and uneven illumination, and drastic light fluctuation in the video. In this stud...Single-feature methods are unable to effectively track a target in an underground coal mine video due to the high background noise, low and uneven illumination, and drastic light fluctuation in the video. In this study, we propose an underground coal mine personnel target tracking method using multi-feature joint sparse representation. First, with a particle filter framework, the global and local multiple features of the target template and candidate particles are extracted. Second, each of the candidate particles is sparsely represented by a dictionary template, and reconstruction is achieved after solving the sparse coefficient. Last, the particle with the lowest reconstruction error is deemed the tracking result. To validate the effectiveness of the proposed algorithm, we compare the proposed method with three commonly employed tracking algorithms. The results show that the proposed method is able to reliably track the target in various scenarios, such as occlusion and illumination change, which generates better tracking results and validates the feasibility and effectiveness of the proposed method.展开更多
针对已有方法对机场道面地下管线定位误差较大的问题,提出一种机场道面地下管线三维定位算法。首先,对探地雷达所成管线的B-scan图像进行预处理,将处理后的图像输入Faster-RCNN网络中,对B-scan图像中的管线进行目标识别;其次,由于管线...针对已有方法对机场道面地下管线定位误差较大的问题,提出一种机场道面地下管线三维定位算法。首先,对探地雷达所成管线的B-scan图像进行预处理,将处理后的图像输入Faster-RCNN网络中,对B-scan图像中的管线进行目标识别;其次,由于管线目标符合双曲线形态特征,采用双曲线顶点获取算法确定顶点位置;最后,设计三维空间直线拟合(three-dimensional space line fitting,TDSLF)算法来判断地下管线的具体位置,进行地下管线的三维重构。所提算法实现了地下管线的自动识别与定位,与真实机场道面地下管线实际位置的最大误差仅为4 cm。展开更多
煤矿井下光照不均、照度低且粉尘大,视频成像夹杂着噪声,进行视频监测时存在遮挡以及误检率高等问题。为保障井下人员安全,提出一种改进的DeepSORT目标跟踪算法,实现对矿井人员的跟踪。首先,选用OSNet全尺度网络优化浅层残差网络,提高...煤矿井下光照不均、照度低且粉尘大,视频成像夹杂着噪声,进行视频监测时存在遮挡以及误检率高等问题。为保障井下人员安全,提出一种改进的DeepSORT目标跟踪算法,实现对矿井人员的跟踪。首先,选用OSNet全尺度网络优化浅层残差网络,提高表观特征提取能力;其次,优化交并比(Intersection over Union,Io U)匹配方式,采用完全交并比(Complete Intersection over Union,CIo U)匹配方式判断检测框与边界回归之间的匹配程度;最后,基于Python平台对改进后的跟踪算法进行仿真验证,检验算法的有效性。实验结果表明,发生遮挡时,与DeepSORT算法相比,改进算法增强了模型的健壮性,具有更好的跟踪效果。展开更多
文摘Single-feature methods are unable to effectively track a target in an underground coal mine video due to the high background noise, low and uneven illumination, and drastic light fluctuation in the video. In this study, we propose an underground coal mine personnel target tracking method using multi-feature joint sparse representation. First, with a particle filter framework, the global and local multiple features of the target template and candidate particles are extracted. Second, each of the candidate particles is sparsely represented by a dictionary template, and reconstruction is achieved after solving the sparse coefficient. Last, the particle with the lowest reconstruction error is deemed the tracking result. To validate the effectiveness of the proposed algorithm, we compare the proposed method with three commonly employed tracking algorithms. The results show that the proposed method is able to reliably track the target in various scenarios, such as occlusion and illumination change, which generates better tracking results and validates the feasibility and effectiveness of the proposed method.
文摘针对已有方法对机场道面地下管线定位误差较大的问题,提出一种机场道面地下管线三维定位算法。首先,对探地雷达所成管线的B-scan图像进行预处理,将处理后的图像输入Faster-RCNN网络中,对B-scan图像中的管线进行目标识别;其次,由于管线目标符合双曲线形态特征,采用双曲线顶点获取算法确定顶点位置;最后,设计三维空间直线拟合(three-dimensional space line fitting,TDSLF)算法来判断地下管线的具体位置,进行地下管线的三维重构。所提算法实现了地下管线的自动识别与定位,与真实机场道面地下管线实际位置的最大误差仅为4 cm。
文摘煤矿井下光照不均、照度低且粉尘大,视频成像夹杂着噪声,进行视频监测时存在遮挡以及误检率高等问题。为保障井下人员安全,提出一种改进的DeepSORT目标跟踪算法,实现对矿井人员的跟踪。首先,选用OSNet全尺度网络优化浅层残差网络,提高表观特征提取能力;其次,优化交并比(Intersection over Union,Io U)匹配方式,采用完全交并比(Complete Intersection over Union,CIo U)匹配方式判断检测框与边界回归之间的匹配程度;最后,基于Python平台对改进后的跟踪算法进行仿真验证,检验算法的有效性。实验结果表明,发生遮挡时,与DeepSORT算法相比,改进算法增强了模型的健壮性,具有更好的跟踪效果。
