DensePeds: Pedestrian Tracking in Dense Crowds Using Front-RVO and Sparse Features
Rohan Chandra, Uttaran Bhattacharya, Aniket Bera, Dinesh Manocha
Abstract
We present a pedestrian tracking algorithm, DensePeds, that tracks individuals in highly dense crowds (>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 × 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.
BibTeX
@inproceedings{iros2019_densepedspedestr,
title = {DensePeds: Pedestrian Tracking in Dense Crowds Using Front-RVO and Sparse Features},
author = {Rohan Chandra and Uttaran Bhattacharya and Aniket Bera and Dinesh Manocha},
booktitle = {IROS 2019},
year = {2019}
}