Measuring indoor occupancy in intelligent buildings using the fusion of vision sensors

Abstract

In intelligent buildings, practical sensing systems designed to gather indoor occupancy information play an indispensable role in improving occupant comfort and energy efficiency. In this paper, we propose a novel method for occupancy measurement based on the video surveillance now widely used in buildings. In our method, we analyze occupant detection both at the entrance and inside the room. A two-stage static detector is presented based on both appearances and shapes to find the human heads in rooms, and motion-based technology is used for occupant detection at the entrance. To model the change of occupancy and combine the detection results from multiple vision sensors located at entrances and inside rooms for more accurate occupancy estimation, we propose a dynamic Bayesian network-based method. The detection results of each vision sensor play the role of evidence nodes of this network, and thus, we can estimate the true occupancy at time t using the evidence prior to (and including) time t. Experimental results demonstrate the effectiveness and efficiency of the proposed method.

Monitoring of the daily living activities in smart home care

“…According to Zhao, occupancy of anomalous user behavior tends to be figured out from multiple time-series records of occupancy [43]. For prediction and subsequent classification of automatic human activity recognition (AR), the regression method of Artifical Neural Networks (ANN), Hidden Markov models [43][44][45] decision trees method [46], methods using Bayesian networks [47], Conditional Random Fields (CRF) or a sequential Markov Logic Network (MLN) [48] can be used.…”

The design of an indirect method for the human presence monitoring in the intelligent building

“…To determine room occupancy in an IAB, a wide range of sensors can be used, for example rH sensors, wearable inertial sensors or wearable vital signs sensors [14], humidity sensors [15], RFID sensors [16], microphones [17], movement sensors [18], video surveillance [19], light sensor [20], presence sensors [21], CO 2 sensors [22], ambient sensors [23], video cameras [24], simple binary sensors or temperature sensors etc. Two approaches can be used for monitoring the occupancy of individual rooms in an administrative building [25]:…”

Non-invasive (non-contact) measurements of human thermal physiology signals and thermal comfort/discomfort poses -A review

“…Recognition algorithm, based on convolutional neural network, can achieve a detection rate of 95.2% for human head-shoulder targets (Zou et al, 2017). Multiple vision sensors, aided by Bayesian algorithm data fusion, can improve sensing accuracy (Liu et al, 2013). Above mentioned studies were mainly focused on occupants' positioning, without obtaining human poses which reflected operating modes of multi-functional rooms.…”