# DensePeds: Pedestrian Tracking in Dense Crowds Using Front-RVO and Sparse Features

## TL;DR

DensePeds is a novel pedestrian tracking algorithm designed for dense crowds, combining a new motion model and sparse features to improve accuracy and speed in challenging scenarios.

## Contribution

Introduces Front-RVO, a new collision avoidance-based motion model, and integrates it with Mask R-CNN for enhanced dense crowd pedestrian tracking.

## Key findings

- 4.5 times faster than previous methods on MOT benchmark
- Achieves over 2.6% improvement in dense crowd tracking accuracy
- Performs well on both standard and new dense crowd datasets

## Abstract

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.