Deep Multi-camera People Detection

TL;DR

This paper introduces a deep learning approach for multi-camera people detection that leverages monocular datasets and joint multi-view processing, significantly improving accuracy over existing methods.

Contribution

It presents a novel end-to-end deep learning architecture for multi-view people detection that utilizes monocular datasets and parallel multi-stream processing.

Findings

Outperforms existing methods on PETS 2009 dataset

Provides a new three-camera HD dataset for research

Open-sources code and trained models

Abstract

This paper addresses the problem of multi-view people occupancy map estimation. Existing solutions for this problem either operate per-view, or rely on a background subtraction pre-processing. Both approaches lessen the detection performance as scenes become more crowded. The former does not exploit joint information, whereas the latter deals with ambiguous input due to the foreground blobs becoming more and more interconnected as the number of targets increases. Although deep learning algorithms have proven to excel on remarkably numerous computer vision tasks, such a method has not been applied yet to this problem. In large part this is due to the lack of large-scale multi-camera data-set. The core of our method is an architecture which makes use of monocular pedestrian data-set, available at larger scale then the multi-view ones, applies parallel processing to the multiple video streams, and jointly utilises it. Our end-to-end deep learning method outperforms existing methods by large margins on the commonly used PETS 2009 data-set. Furthermore, we make publicly available a new three-camera HD data-set. Our source code and trained models will be made available under an open-source license.