MSN: Multi-Style Network for Trajectory Prediction

• URL: http://arxiv.org/abs/2107.00932v5
• Date: Mon, 8 May 2023 07:30:35 GMT
• Title: MSN: Multi-Style Network for Trajectory Prediction
• Authors: Conghao Wong, Beihao Xia, Qinmu Peng, Wei Yuan and Xinge You
• Abstract summary: Trajectory prediction aims to forecast agents' possible future locations considering their observations along with the video context. This paper proposes the Multi-Style Network (MSN), which utilizes style proposal and stylized prediction using two sub-networks. Experiments show that the proposed MSN outperforms current state-of-the-art methods up to 10% quantitatively on two widely used datasets.
• Score: 14.861532983777133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory prediction aims to forecast agents' possible future locations considering their observations along with the video context. It is strongly needed by many autonomous platforms like tracking, detection, robot navigation, and self-driving cars. Whether it is agents' internal personality factors, interactive behaviors with the neighborhood, or the influence of surroundings, they all impact agents' future planning. However, many previous methods model and predict agents' behaviors with the same strategy or feature distribution, making them challenging to make predictions with sufficient style differences. This paper proposes the Multi-Style Network (MSN), which utilizes style proposal and stylized prediction using two sub-networks, to provide multi-style predictions in a novel categorical way adaptively. The proposed network contains a series of style channels, and each channel is bound to a unique and specific behavior style. We use agents' end-point plannings and their interaction context as the basis for the behavior classification, so as to adaptively learn multiple diverse behavior styles through these channels. Then, we assume that the target agents may plan their future behaviors according to each of these categorized styles, thus utilizing different style channels to make predictions with significant style differences in parallel. Experiments show that the proposed MSN outperforms current state-of-the-art methods up to 10% quantitatively on two widely used datasets, and presents better multi-style characteristics qualitatively.

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