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Surveillance cameras are widely applied for indoor occupancy measurement andhuman movement perception, which benefit for building energy management andsocial security. To address the challenges of limited view angle of singlecamera as well as lacking of inter-camera collaboration, this study presents anon-overlapping multi-camera system to enlarge the surveillance area anddevotes to retrieve the same person appeared from different camera views. Thesystem is deployed in an office building and four-day videos are collected. Bytraining a deep convolutional neural network, the proposed system firstextracts the appearance feature embeddings of each personal image, whichdetected from different cameras, for similarity comparison. Then, a stochasticinter-camera transition matrix is associated with appearance feature forfurther improving the person re-identification ranking results. Finally, anoise-suppression explanation is given for analyzing the matching improvements.This paper expands the scope of indoor movement perception based onnon-overlapping multiple cameras and improves the accuracy of pedestrianre-identification without introducing additional types of sensors.

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