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
In order to avoid the problem of poor illumination characteristics and inaccurate positioning accuracy, this paper proposed a pedestrian detection algorithm suitable for low-light environments. The algorithm first app...In order to avoid the problem of poor illumination characteristics and inaccurate positioning accuracy, this paper proposed a pedestrian detection algorithm suitable for low-light environments. The algorithm first applied the multi-scale Retinex image enhancement algorithm to the sample pre-processing of deep learning to improve the image resolution. Then the paper used the faster regional convolutional neural network to train the pedestrian detection model, extracted the pedestrian characteristics, and obtained the bounding boxes through classification and position regression. Finally, the pedestrian detection process was carried out by introducing the Soft-NMS algorithm, and the redundant bounding box was eliminated to obtain the best pedestrian detection position. The experimental results showed that the proposed detection algorithm achieves an average accuracy of 89.74% on the low-light dataset, and the pedestrian detection effect was more significant.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In order to avoid the problem of poor illumination characteristics and inaccurate positioning accuracy, this paper proposed a pedestrian detection algorithm suitable for low-light environments. The algorithm first applied the multi-scale Retinex image enhancement algorithm to the sample pre-processing of deep learning to improve the image resolution. Then the paper used the faster regional convolutional neural network to train the pedestrian detection model, extracted the pedestrian characteristics, and obtained the bounding boxes through classification and position regression. Finally, the pedestrian detection process was carried out by introducing the Soft-NMS algorithm, and the redundant bounding box was eliminated to obtain the best pedestrian detection position. The experimental results showed that the proposed detection algorithm achieves an average accuracy of 89.74% on the low-light dataset, and the pedestrian detection effect was more significant.
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%。
