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

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.

1 Introduction

With burgeoning population and rapid urbanization, crowd gatherings have become more prominent in the recent years. Consequently, computer vision-based crowd analytics and surveillance [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17] have received increased interest. Furthermore, algorithms developed for the purpose of crowd analytics have found applications in other fields such as agriculture monitoring [18], microscopic biology [19], urban planning and environmental survey [2], [20]. Current state-of-the-art counting networks achieve impressive error rates on a variety of datasets that contain numerous challenges. Their success can be broadly attributed to two major factors: (i) development and publication of challenging datasets [3], [4], [5], [21], and (ii) design of novel convolutional neural network (CNN) architectures specifically for improving count performance [4], [7], [22], [23], [24], [25], [26], [27]. In this paper, we consider both of the above factors in an attempt to further improve the crowd counting performance.