Top-Down Feedback for Crowd Counting Convolutional Neural Network

TL;DR

This paper introduces a top-down feedback mechanism in CNNs for crowd counting, enhancing spatial context understanding and reducing false predictions in dense crowd scenarios.

Contribution

It presents a novel architecture combining bottom-up and top-down CNNs with feedback gating to improve crowd density estimation accuracy.

Findings

Significant performance improvement on major crowd datasets

Effective correction of initial density predictions

Enhanced spatial context utilization

Abstract

Counting people in dense crowds is a demanding task even for humans. This is primarily due to the large variability in appearance of people. Often people are only seen as a bunch of blobs. Occlusions, pose variations and background clutter further compound the difficulty. In this scenario, identifying a person requires larger spatial context and semantics of the scene. But the current state-of-the-art CNN regressors for crowd counting are feedforward and use only limited spatial context to detect people. They look for local crowd patterns to regress the crowd density map, resulting in false predictions. Hence, we propose top-down feedback to correct the initial prediction of the CNN. Our architecture consists of a bottom-up CNN along with a separate top-down CNN to generate feedback. The bottom-up network, which regresses the crowd density map, has two columns of CNN with different receptive fields. Features from various layers of the bottom-up CNN are fed to the top-down network. The feedback, thus generated, is applied on the lower layers of the bottom-up network in the form of multiplicative gating. This masking weighs activations of the bottom-up network at spatial as well as feature levels to correct the density prediction. We evaluate the performance of our model on all major crowd datasets and show the effectiveness of top-down feedback.