Unveiling personnel movement in a larger indoor area with a non-overlapping multi-camera system

TL;DR

This paper introduces a multi-camera system that enhances indoor person re-identification accuracy by combining deep learning features and transition matrices, expanding surveillance capabilities without extra sensors.

Contribution

It presents a novel non-overlapping multi-camera system with a deep learning approach and transition matrix for improved indoor person re-identification.

Findings

Improved re-identification accuracy in indoor environments.

Effective use of appearance features and transition matrices.

No additional sensors required for enhanced surveillance.

Abstract

Surveillance cameras are widely applied for indoor occupancy measurement and human movement perception, which benefit for building energy management and social security. To address the challenges of limited view angle of single camera as well as lacking of inter-camera collaboration, this study presents a non-overlapping multi-camera system to enlarge the surveillance area and devotes to retrieve the same person appeared from different camera views. The system is deployed in an office building and four-day videos are collected. By training a deep convolutional neural network, the proposed system first extracts the appearance feature embeddings of each personal image, which detected from different cameras, for similarity comparison. Then, a stochastic inter-camera transition matrix is associated with appearance feature for further improving the person re-identification ranking results. Finally, a noise-suppression explanation is given for analyzing the matching improvements. This paper expands the scope of indoor movement perception based on non-overlapping multiple cameras and improves the accuracy of pedestrian re-identification without introducing additional types of sensors.