目的时序动作检测(temporal action detection)作为计算机视觉领域的一个热点课题,其目的是检测视频中动作发生的具体区间,并确定动作的类别。这一课题在现实生活中具有深远的实际意义。如何在长视频中快速定位且实现时序动作检测仍然...目的时序动作检测(temporal action detection)作为计算机视觉领域的一个热点课题,其目的是检测视频中动作发生的具体区间,并确定动作的类别。这一课题在现实生活中具有深远的实际意义。如何在长视频中快速定位且实现时序动作检测仍然面临挑战。为此,本文致力于定位并优化动作发生时域的候选集,提出了时域候选区域优化的时序动作检测方法TPO(temporal proposal optimization)。方法采用卷积神经网络(convolutional neural network,CNN)和双向长短期记忆网络(bidirectional long short term memory,BLSTM)来捕捉视频的局部时序关联性和全局时序信息;并引入联级时序分类优化(connectionist temporal classification,CTC)方法,评估每个时序位置的边界概率和动作概率得分;最后,融合两者的概率得分曲线,优化时域候选区域候选并排序,最终实现时序上的动作检测。结果在Activity Net v1.3数据集上进行实验验证,TPO在各评价指标,如一定时域候选数量下的平均召回率AR@100(average recall@100),曲线下的面积AUC(area under a curve)和平均均值平均精度m AP(mean average precision)上分别达到74.66、66.32、30.5,而各阈值下的均值平均精度m AP@Io U(m AP@intersection over union)在阈值为0.75和0.95时也分别达到了30.73和8.22,与SSN(structured segment network)、TCN(temporal context network)、Prop-SSAD(single shot action detector for proposal)、CTAP(complementary temporal action proposal)和BSN(boundary sensitive network)等方法相比,TPO的所有性能指标均有提高。结论本文提出的模型兼顾了视频的全局时序信息和局部时序信息,使得预测的动作候选区域边界更为准确和灵活,同时也验证了候选区域的准确性能够有效提高时序动作检测的精确度。展开更多
The advancements in distributed generation(DG)technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems.However,the intermittent nature of renewable e...The advancements in distributed generation(DG)technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems.However,the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties.This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory(BiLSTM)neural networks,while handling the high dimensionality in weight parameters using variational auto-encoders(VAE).The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error(RMSE),Pinball loss,etc.Furthermore,reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component.When compared with benchmark methods,the proposed method leads to significant improvements in weight reduction,i.e.,from 76,4224 to 2,022 number of weight parameters,quantifying to 97.35%improvement in weight parameters reduction and 37.93%improvement in computational time for 6 months of solar power generation data.展开更多
文摘目的时序动作检测(temporal action detection)作为计算机视觉领域的一个热点课题,其目的是检测视频中动作发生的具体区间,并确定动作的类别。这一课题在现实生活中具有深远的实际意义。如何在长视频中快速定位且实现时序动作检测仍然面临挑战。为此,本文致力于定位并优化动作发生时域的候选集,提出了时域候选区域优化的时序动作检测方法TPO(temporal proposal optimization)。方法采用卷积神经网络(convolutional neural network,CNN)和双向长短期记忆网络(bidirectional long short term memory,BLSTM)来捕捉视频的局部时序关联性和全局时序信息;并引入联级时序分类优化(connectionist temporal classification,CTC)方法,评估每个时序位置的边界概率和动作概率得分;最后,融合两者的概率得分曲线,优化时域候选区域候选并排序,最终实现时序上的动作检测。结果在Activity Net v1.3数据集上进行实验验证,TPO在各评价指标,如一定时域候选数量下的平均召回率AR@100(average recall@100),曲线下的面积AUC(area under a curve)和平均均值平均精度m AP(mean average precision)上分别达到74.66、66.32、30.5,而各阈值下的均值平均精度m AP@Io U(m AP@intersection over union)在阈值为0.75和0.95时也分别达到了30.73和8.22,与SSN(structured segment network)、TCN(temporal context network)、Prop-SSAD(single shot action detector for proposal)、CTAP(complementary temporal action proposal)和BSN(boundary sensitive network)等方法相比,TPO的所有性能指标均有提高。结论本文提出的模型兼顾了视频的全局时序信息和局部时序信息,使得预测的动作候选区域边界更为准确和灵活,同时也验证了候选区域的准确性能够有效提高时序动作检测的精确度。
文摘The advancements in distributed generation(DG)technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems.However,the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties.This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory(BiLSTM)neural networks,while handling the high dimensionality in weight parameters using variational auto-encoders(VAE).The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error(RMSE),Pinball loss,etc.Furthermore,reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component.When compared with benchmark methods,the proposed method leads to significant improvements in weight reduction,i.e.,from 76,4224 to 2,022 number of weight parameters,quantifying to 97.35%improvement in weight parameters reduction and 37.93%improvement in computational time for 6 months of solar power generation data.
