DensePeds: 正面RVOとスパース特徴を用いた高密度群衆の歩行者追跡
DensePeds: Pedestrian Tracking in Dense Crowds Using Front-RVO and Sparse Features
衝突回避制約に基づく新しい運動モデルFront-RVOとMask R-CNNのスパース特徴を組み合わせ、1平方メートルあたり2人以上の高密度群衆でも個々の歩行者を追跡するアルゴリズムを提案した。従来手法より4.5倍高速で、高密度動画での精度も向上している。
著者: Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a pedestrian tracking algorithm, DensePeds, that tracks individuals in highly dense crowds (greater than 2 pedestrians per square meter). Our approach is designed for videos captured from front-facing or elevated cameras. We present a new motion model called Front-RVO (FRVO) for predicting pedestrian movements in dense situations using collision avoidance constraints and combine it with state-of-the-art Mask R-CNN to compute sparse feature vectors that reduce the loss of pedestrian tracks (false negatives). We evaluate DensePeds on the standard MOT benchmarks as well as a new dense crowd dataset. In practice, our approach is 4.5 times faster than prior tracking algorithms on the MOT benchmark and we are state-of-the-art in dense crowd videos by over 2.6% on the absolute scale on average.
