動きのパターンに着目：新しい畳み込み演算子を用いた時系列CNNによる人間軌道予測
Noticing Motion Patterns: Temporal CNN with a Novel Convolution Operator for Human Trajectory Prediction
軌道データの時系列パターンを学習・検出するSocial-PECという畳み込み演算子を提案し、人間軌道予測で従来のプーリング層の曖昧さを解消しつつ最先端に匹敵する性能を示した論文。
著者: Dapeng Zhao, Jean Oh
分類: cs.CV, cs.RO
原文アブストラクト
We propose a Convolutional Neural Network-based approach to learn, detect,and extract patterns in sequential trajectory data, known here as Social Pattern Extraction Convolution (Social-PEC). A set of experiments carried out on the human trajectory prediction problem shows that our model performs comparably to the state of the art and outperforms in some cases. More importantly,the proposed approach unveils the obscurity in the previous use of pooling layer, presenting a way to intuitively explain the decision-making process.
