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AONet: Active Offset Network for crowd flow prediction
Engineering Applications of Artificial Intelligence ( IF 8 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.engappai.2020.104022
Dafeng Wang , Qian Ma , Naiyao Wang , Xuanzhe Fan , Mingyu Lu , Hongbo Liu

Predicting crowd flow is of great importance to public safety and traffic management. The crowd flow is difficult to predict accurately and timely due to the uncertainty of the future positions. In this paper, we propose a novel Active Offset Network (AONet), in which ActiveGRU (Active Gate Recurrent Unit) is designed to predict the variation of pedestrians’ positions in the crowd flow. Its inner location-variant recurrent structure is implemented by utilizing convolution operation on low dimensional spatio-temporal sequences to obtain fractional offset locations. Afterwards, the sampling locations are determined by bilinear interpolation on fractional offset locations. Moreover, a probabilistic sparse strategy is introduced to reduce the links between sampling locations during supervised training. Finally, the experiments over popular benchmarks demonstrate that our method can actively characterize the future positions of pedestrians. Meanwhile, the performance of the proposed AONet is superior over state-of-art baselines with regard to both accuracy and computational savings.



中文翻译:

AONet:用于人群流量预测的主动补偿网络

预测人群流量对公共安全和交通管理至关重要。由于未来职位的不确定性,难以准确,及时地预测人群流量。在本文中,我们提出了一种新颖的主动偏置网络(AONet),其中设计了ActiveGRU(主动门循环单元)来预测行人在人群流中位置的变化。它的内部位置可变递归结构是通过对低维时空序列进行卷积运算以获得分数偏移位置来实现的。然后,通过在分数偏移位置上进行双线性插值来确定采样位置。此外,引入了一种概率稀疏策略,以减少有监督训练期间采样位置之间的联系。最后,通过流行基准进行的实验表明,我们的方法可以主动表征行人的未来位置。同时,在准确性和计算节省方面,拟议的AONet的性能均优于最新的基准。

更新日期:2020-11-02
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