Deep Multiple Instance Learning for Human Trajectory Identification

Author:

Fan Zipei1,Chen Quanjun1,Jiang Renhe1,Shibasaki Ryosuke1,Song Xuan2,Tsubouchi Kota3

Affiliation:

1. University of Tokyo, Kashiwa, Chiba, Japan

2. SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech) Shenzhen, China, University of Tokyo, Kashiwa, Chiba, Japan

3. Yahoo Japan Corporation, Tokyo, Japan

Publisher

ACM

Reference14 articles.

1. Review and Perspective for Distance-Based Clustering of Vehicle Trajectories

2. Identifying Human Mobility via Trajectory Embeddings

3. Felix Hausdorff. 1978. Grundzüge der mengenlehre. Vol. 61. American Mathematical Soc. Felix Hausdorff. 1978. Grundzüge der mengenlehre. Vol. 61. American Mathematical Soc.

4. Long Short-Term Memory

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1. LTP-Net: Life-Travel Pattern Based Human Mobility Signature Identification;IEEE Transactions on Intelligent Transportation Systems;2023-12

2. Continuous trajectory similarity search with result diversification;Future Generation Computer Systems;2023-06

3. MTUL: A Novel Approach for Multi-Trajectory User Linking;2022 9th International Conference on Networking, Systems and Security;2022-12-20

4. Towards robust trajectory similarity computation: Representation-based spatio-temporal similarity quantification;World Wide Web;2022-08-09

5. Is Reinforcement Learning the Choice of Human Learners?;Proceedings of the 28th International Conference on Advances in Geographic Information Systems;2020-11-03

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