  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.
