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Your search keyword '"Yin, Baocai"' showing total 17 results
Start Over Author "Yin, Baocai" Journal ieee transactions on intelligent transportation systems
17 results on '"Yin, Baocai"'

1. Multi-Level Dynamic Graph Convolutional Networks for Weakly Supervised Crowd Counting

Author

Miao, Zhuangzhuang, Zhang, Yong, Ren, Hao, Hu, Yongli, and Yin, Baocai

Abstract

Crowd counting is very important in many fields such as public safety, urban planning, and is essential for the intelligent transportation systems. Due to the complexity and diversity of traffic scenes, point-level annotations for pedestrians would cost much human labor. Weakly supervised crowd counting methods are more suitable for these scenes, considering they only require count-level annotations. However, ignoring the uneven distribution of cross-distance crowd region density and multi-scale pedestrian head, existing weakly supervised methods can not achieve similar counting performance as fully supervised crowd counting methods. To solve these issues, we propose a novel multi-level dynamic graph convolutional networks for weakly supervised crowd counting. Within this network, a multi-level region dynamic graph convolutional module is designed to mine the cross-distance intrinsic relationship between crowd regions. A feature enhancement module is used to enhance crowd semantic information. In addition, we design a coarse grained multi-level feature fusion module to aggregate multi-scale pedestrian information. Experiments are conducted on five well-known benchmark crowd counting datasets, achieving state-of-the-art results compared to existing weakly supervised methods and competitive results compared to fully supervised methods.

Published
2024
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12. TCSA-Net: A Temporal-Context-Based Self-Attention Network for Next Location Prediction.

Author

Sun, Guiming, Qi, Heng, Shen, Yanming, and Yin, Baocai

Abstract

Next location prediction aims to find the location that the user will visit next. It plays a fundamental role for location-based applications. However, the heterogeneity and sparsity of the trajectory data pose great challenges to the task. Recently, RNN-based methods have shown promising performance in learining the spatio-temporal characteristics of the trajectory. While the effectiveness of location prediction has been improved, the computational efficiency and the long-term preferences still leave space for further research. The self-attention mechanism is viewed as a promising solution for parallel computation and exploiting sequential regularities from sparse data. But the huge memory cost and the neglect of temporal information make it infeasible to directly modeling human mobility regularities. In this paper, we propose a temporal-context-based self-attention network named TCSA-Net, which can simultaneously exploit long- and short-term mvoement preferences from sparse and long trajectories. In particular, we design a novel two-stage self-attention architecture that can learn long-term dependency under constrained memory budget. Further, we propose a multi-modal embedding layer to model two complementary temporal contexts and provide more abundant temporal and sequential information. Extensive experiments on two real-life datasets show that the TCSA-Net significantly outperforms the state-of-the-art methods in terms of standard evaluation metrics. [ABSTRACT FROM AUTHOR]

Published
2022
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