Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs

TL;DR

This paper introduces CP-CNN, a novel deep learning architecture that combines global and local contextual information to generate high-quality crowd density maps and improve counting accuracy.

Contribution

The paper proposes a new multi-module CNN architecture that explicitly incorporates global and local context for crowd density estimation, achieving superior results.

Findings

Significant improvement over state-of-the-art methods

High-resolution, high-quality density maps generated

Effective end-to-end training with adversarial and Euclidean losses

Abstract

We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-quality crowd density and count estimation by explicitly incorporating global and local contextual information of crowd images. The proposed CP-CNN consists of four modules: Global Context Estimator (GCE), Local Context Estimator (LCE), Density Map Estimator (DME) and a Fusion-CNN (F-CNN). GCE is a VGG-16 based CNN that encodes global context and it is trained to classify input images into different density classes, whereas LCE is another CNN that encodes local context information and it is trained to perform patch-wise classification of input images into different density classes. DME is a multi-column architecture-based CNN that aims to generate high-dimensional feature maps from the input image which are fused with the contextual information estimated by GCE and LCE using F-CNN. To generate high resolution and high-quality density maps, F-CNN uses a set of convolutional and fractionally-strided convolutional layers and it is trained along with the DME in an end-to-end fashion using a combination of adversarial loss and pixel-level Euclidean loss. Extensive experiments on highly challenging datasets show that the proposed method achieves significant improvements over the state-of-the-art methods.