MonoPIC：IoTエッジ向け単眼低遅延歩行者意図分類フレームワーク（ID3決定木モデル使用）
MonoPIC -- A Monocular Low-Latency Pedestrian Intention Classification Framework for IoT Edges Using ID3 Modelled Decision Trees
MediaPipeの姿勢推定から得たクォータニオンを用いて、単眼2D画像中の歩行者の意図をID3決定木で分類する低遅延手法を提案し、平均48msの遅延で83.56%の精度を達成した。
著者: Sriram Radhakrishna, Adithya Balasubramanyam
分類: cs.RO, cs.CV
原文アブストラクト
Road accidents involving autonomous vehicles commonly occur in situations where a (pedestrian) obstacle presents itself in the path of the moving vehicle at very sudden time intervals, leaving the robot even lesser time to react to the change in scene. In order to tackle this issue, we propose a novel algorithmic implementation that classifies the intent of a single arbitrarily chosen pedestrian in a two dimensional frame into logic states in a procedural manner using quaternions generated from a MediaPipe pose estimation model. This bypasses the need to employ any relatively high latency deep-learning algorithms primarily due to the lack of necessity for depth perception as well as an implicit cap on the computational resources that most IoT edge devices present. The model was able to achieve an average testing accuracy of 83.56% with a reliable variance of 0.0042 while operating with an average latency of 48 milliseconds, demonstrating multiple notable advantages over the current standard of using spatio-temporal convolutional networks for these perceptive tasks.
