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

by   Vishwanath A. Sindagi, et al.

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.


page 1

page 2

page 5

page 6

page 7

page 8

page 11


Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method

In this work, we propose a novel crowd counting network that progressive...

Inverse Attention Guided Deep Crowd Counting Network

In this paper, we address the challenging problem of crowd counting in c...

NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting

In the last decade, crowd counting attracts much attention of researcher...

ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification

In this paper we propose ResnetCrowd, a deep residual architecture for s...

HA-CCN: Hierarchical Attention-based Crowd Counting Network

Single image-based crowd counting has recently witnessed increased focus...

CrowdNet: A Deep Convolutional Network for Dense Crowd Counting

Our work proposes a novel deep learning framework for estimating crowd d...

Crowd Counting using Deep Recurrent Spatial-Aware Network

Crowd counting from unconstrained scene images is a crucial task in many...

Please sign up or login with your details

Forgot password? Click here to reset