Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label c...Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label convolutional neural network( MSMLCNN) is proposed to predict multiple pedestrian attributes simultaneously. The pedestrian attribute classification problem is firstly transformed into a multi-label problem including multiple binary attributes needed to be classified. Then,the multi-label problem is solved by fully connecting all binary attributes to multi-scale features with logistic regression functions. Moreover,the multi-scale features are obtained by concatenating those featured maps produced from multiple pooling layers of the MSMLCNN at different scales. Extensive experiment results show that the proposed MSMLCNN outperforms state-of-the-art pedestrian attribute classification methods with a large margin.展开更多
针对行人轨迹预测研究中仅关注历史轨迹的交互信息,而忽略了终点交互信息的问题,提出一种基于图卷积网络(GCN)和终点诱导(Endpoint Induction)的行人轨迹预测模型GCN-EI。首先,在训练集上使用分类方法学习行人未来可能的加权终点分布;其...针对行人轨迹预测研究中仅关注历史轨迹的交互信息,而忽略了终点交互信息的问题,提出一种基于图卷积网络(GCN)和终点诱导(Endpoint Induction)的行人轨迹预测模型GCN-EI。首先,在训练集上使用分类方法学习行人未来可能的加权终点分布;其次,将可能的终点与它们对应的历史轨迹相连接,并使用基于注意力机制和终点条件的GCN在更长的时间跨度上提取行人的交互特征,同时使用个体特征模块提取行人的内在运动特征;最后通过时间内推卷积预测行人的未来轨迹。在ETH和UCY数据集上对模型进行的测试结果表明,相较于STITD-GCN(SpatioTemporal Interaction and Trajectory Distribution GCN)模型,所提模型在平均位移误差(ADE)和最终位移误差(FDE)上分别下降了4.5%和5.0%;相较于采用分类方法的PCCSNet(Prediction via modality Clustering, Classification and Synthesis Network)模型,在FDE上下降了9.5%。展开更多
基金Supported by the National Natural Science Foundation of China(No.61602191,61672521,61375037,61473291,61572501,61572536,61502491,61372107,61401167)the Natural Science Foundation of Fujian Province(No.2016J01308)+3 种基金the Scientific and Technology Funds of Quanzhou(No.2015Z114)the Scientific and Technology Funds of Xiamen(No.3502Z20173045)the Promotion Program for Young and Middle aged Teacher in Science and Technology Research of Huaqiao University(No.ZQN-PY418,ZQN-YX403)the Scientific Research Funds of Huaqiao University(No.16BS108)
文摘Pedestrian attribute classification from a pedestrian image captured in surveillance scenarios is challenging due to diverse clothing appearances,varied poses and different camera views. A multiscale and multi-label convolutional neural network( MSMLCNN) is proposed to predict multiple pedestrian attributes simultaneously. The pedestrian attribute classification problem is firstly transformed into a multi-label problem including multiple binary attributes needed to be classified. Then,the multi-label problem is solved by fully connecting all binary attributes to multi-scale features with logistic regression functions. Moreover,the multi-scale features are obtained by concatenating those featured maps produced from multiple pooling layers of the MSMLCNN at different scales. Extensive experiment results show that the proposed MSMLCNN outperforms state-of-the-art pedestrian attribute classification methods with a large margin.
文摘针对行人轨迹预测研究中仅关注历史轨迹的交互信息,而忽略了终点交互信息的问题,提出一种基于图卷积网络(GCN)和终点诱导(Endpoint Induction)的行人轨迹预测模型GCN-EI。首先,在训练集上使用分类方法学习行人未来可能的加权终点分布;其次,将可能的终点与它们对应的历史轨迹相连接,并使用基于注意力机制和终点条件的GCN在更长的时间跨度上提取行人的交互特征,同时使用个体特征模块提取行人的内在运动特征;最后通过时间内推卷积预测行人的未来轨迹。在ETH和UCY数据集上对模型进行的测试结果表明,相较于STITD-GCN(SpatioTemporal Interaction and Trajectory Distribution GCN)模型,所提模型在平均位移误差(ADE)和最终位移误差(FDE)上分别下降了4.5%和5.0%;相较于采用分类方法的PCCSNet(Prediction via modality Clustering, Classification and Synthesis Network)模型,在FDE上下降了9.5%。
