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
针对行人轨迹预测中存在的时序特征建模不足、多尺度融合缺乏明确区分以及多任务训练不稳定等问题,提出一种基于矩阵记忆长短期记忆网络(matrix long short-term memory, mLSTM)的纯时序预测算法。该算法构建以mLSTM为核心的编码器-解...针对行人轨迹预测中存在的时序特征建模不足、多尺度融合缺乏明确区分以及多任务训练不稳定等问题,提出一种基于矩阵记忆长短期记忆网络(matrix long short-term memory, mLSTM)的纯时序预测算法。该算法构建以mLSTM为核心的编码器-解码器架构,挖掘轨迹的时间依赖特征;设计多尺度轨迹特征融合模块,采用双向策略实现短期与长期特征的层次化表达;引入指数移动平均标准化的多任务机制,提升训练的稳定性与模型的泛化能力。在ETH和UCY数据集上的实验结果表明,该算法相较于Trajectory-Transformer和SGCN,在平均位移误差上分别降低14.81%和16.21%,在最终位移误差上分别降低19.66%和4.62%,展现出良好的预测精度与鲁棒性,为行人轨迹预测提供稳健有效的基础模型。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
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中存在的时序特征建模不足、多尺度融合缺乏明确区分以及多任务训练不稳定等问题,提出一种基于矩阵记忆长短期记忆网络(matrix long short-term memory, mLSTM)的纯时序预测算法。该算法构建以mLSTM为核心的编码器-解码器架构,挖掘轨迹的时间依赖特征;设计多尺度轨迹特征融合模块,采用双向策略实现短期与长期特征的层次化表达;引入指数移动平均标准化的多任务机制,提升训练的稳定性与模型的泛化能力。在ETH和UCY数据集上的实验结果表明,该算法相较于Trajectory-Transformer和SGCN,在平均位移误差上分别降低14.81%和16.21%,在最终位移误差上分别降低19.66%和4.62%,展现出良好的预测精度与鲁棒性,为行人轨迹预测提供稳健有效的基础模型。
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.
