2021/03/14 by Jianhua Sun, Yuxuan Li, Sun, Jianhua +5 · 3 citations
Computer Science · Social Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Data Management and Algorithms #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2103.07854
openalex publication_date 2021/03/14 · openalex created_date 2021/10/11 · openalex updated_date 2026/07/28
Multimodal prediction results are essential for trajectory prediction task as there is no single correct answer for the future. Previous frameworks can be divided into three categories: regression, generation and classification frameworks. However, these frameworks have weaknesses in different aspects so that they cannot model the multimodal prediction task comprehensively. In this paper, we present a novel insight along with a brand-new prediction framework by formulating multimodal prediction into three steps: modality clustering, classification and synthesis, and address the shortcomings of earlier frameworks. Exhaustive experiments on popular benchmarks have demonstrated that our proposed method surpasses state-of-the-art works even without introducing social and map information. Specifically, we achieve 19.2% and 20.8% improvement on ADE and FDE respectively on ETH/UCY dataset. Our code will be made publicly availabe.