JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method

TL;DR

This paper introduces JHU-CROWD++, a large and diverse crowd counting dataset with challenging conditions, and proposes a novel residual error estimation network that improves counting accuracy.

Contribution

The paper presents a new large-scale crowd counting dataset and a novel deep residual network with uncertainty-guided refinement for improved crowd density estimation.

Findings

The dataset contains 4,372 images with 1.51 million annotations under diverse conditions.

The proposed CG-DRCN network outperforms existing methods on complex crowd datasets.

Significant error reduction achieved with the new network architecture.

Abstract

Due to its variety of applications in the real-world, the task of single image-based crowd counting has received a lot of interest in the recent years. Recently, several approaches have been proposed to address various problems encountered in crowd counting. These approaches are essentially based on convolutional neural networks that require large amounts of data to train the network parameters. Considering this, we introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++) that contains "4,372" images with "1.51 million" annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weather-based degradations and illumination variations, making it a very challenging dataset. Additionally, the dataset consists of a rich set of annotations at both image-level and head-level. Several recent methods are evaluated and compared on this dataset. The dataset can be downloaded from http://www.crowd-counting.com . Furthermore, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map generated by the final layer as a coarse prediction to refine and generate finer density maps in a progressive fashion using residual learning. Additionally, the residual learning is guided by an uncertainty-based confidence weighting mechanism that permits the flow of only high-confidence residuals in the refinement path. The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors.