BEMS 4) 5) [3][4] Anca [5] 35 Bill [6] Anca Bill 2. RFID RF [7] [7] Garg [8] PC RFID [9]RFID RF RF [10] c 2017 Information Processing Society of
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1 1,a) Lighting Control System Based on Trainable Occupancy Detection Using Camera Images and Light Switch Logs Yuka Takahashi 1,a) Masaki Igarashi 1 Hideaki Uchiyama 1 Rin-ichiro Taniguchi 1 Abstract: We propose a lighting control system based on trainable occupancy detection using camera images and light switch logs. Images used in learning need labeling based on occupancy status in a room. This system automatizes collecting and labeling images by using light switch logs in daily life for reducing labeling costs. Misjudged images during the operation of lighting control system are collected and used in relearning for improving accuracy of lighting control. We constructed our lighting control system and report the results of the system operation in real scene. 1. 1), 2) LED ) 1 Presently with Kyushu University a) yuka takahashi@limu.ait.kyushu-u.ac.jp IoT Energy Management System : EMS EMS EMS EMS HEMSHome Energy Management System[1] BEMSBuilding Energy Management System[2] HEMS c 2017 Information Processing Society of Japan 1
2 BEMS 4) 5) [3][4] Anca [5] 35 Bill [6] Anca Bill 2. RFID RF [7] [7] Garg [8] PC RFID [9]RFID RF RF [10] c 2017 Information Processing Society of Japan 2
3 [11][12] Newsham [12] [13] [14] [15] : 2: 1 IoT 3 c 2017 Information Processing Society of Japan 3
4 ଅ ആ ค ค ԇ 3: Zero-mean Normalized Cross Correlation ZNCCZNCC HOG(Histograms of Oriented Gradients) SVM(Support Vector Machine) [15] [16] Conventional Neural NetworkCNN) CNN CNN ImageNet AlexNet [17][18] c 2017 Information Processing Society of Japan 4
5 ԇ ࠇ ค ԇ ࠇ 4: = Braveridge BTN01 BTN01 Bluetooth MQTT MQTT IoT IBM MOBOTIX Q : 6: 1: CPU Intel(R) Core(TM) i7-6800k 3.40GHz OS Windows 10 Pro RAM 64.0GB GPU NVIDIA GeForce GTX 1080 (8192MB GDDR5X) , c 2017 Information Processing Society of Japan 5
6 情報処理学会研究報告 (a) 在室時 一人 (b) 在室時 複数人 (a) 在室時 (b) 在室時 (c) 不在時 (d) 不在時 片方が点灯 (c) 不在時 (d) 不在時 図 7: 初期学習用画像として取得した画像の例 図 9: 再学習用画像として取得した画像の例 (a) ϭϭϭ ϵϯ ϴϴ ϵϰ ϴϵ ϴϲ ϴϵ ϴϯ ϴϯ ϵϱ ϵϭ ϴϲ ϭϭϭ ϭϭϭ ϵϵ ϭϭϭ ϵθ ϵθ ϵϳ ϴϮ ϵθ ϵϭ ϴϵ ϴϴ ϲϵ ঽ ઈ৷ Ѕ ą (b) 図 8: 不在時画像に誤って在室のラベルがつけられた例 )க 図 10: 消灯操作の精度の変化 たことが考えられる 照明点灯中は画像間に差分が生じる 度に画像を取得しているため 照明変動により画像上に変 化が生じた場合 不在にもかかわらず在室時画像として取 得してしまうことになる なお これらの画像は学習の際 のアンダーサンプリングで除外されていたために学習には 用いられなかった 再学習および消灯操作精度の評価 初期学習用画像に自動消灯操作中に取得した誤判定画像 も含めた学習用画像を用いて 実際に喫茶スペースにおい て自動消灯操作及び誤判定画像を用いた再学習を行った ঽ ઈ৷ Ѕ ą )க 自動消灯期間内で取得できた再学習用画像は 在室時画像 図 11: 随時再学習およびモデル更新を行ったと仮定した場 は 19 枚 不在時画像は 30 枚であった 取得できた再学習 合の消灯操作の精度の変化 用画像の例を図 9 に示す 取得した 19 枚の在室時画像の うち 18 枚は一人で在室しているときのものであった 図 FP=誤って自動消灯を行った回数 9(a) のように 上半身が隠れた状態など 目視でも判別が FN=消灯を行えなかった回数 難しい画像は誤判定することが多かった とおく この時 TP+FP は実際に自動消灯を行った総回 自動消灯操作の精度の評価を行うに当たり TP=正しく自動消灯を行った回数 c 2017 Information Processing Society of Japan 数を TP+FN は消灯すべきだった総回数を表す 自動消 灯を行った総回数と消灯すべきだった総回数それぞれに対 6
7 TP F (%) = (%) = F (%) = TP 100 (1) TP + FP TP 100 (2) TP + FN (3) (a) (b) (a) 12(c)(d) 12(c)(d) 12(a) (c) (a) (d) (a) 12: c 2017 Information Processing Society of Japan 7
8 Ԇଆค ȅଅ Ԇ 13: 1) and new/saving /summary/pdf/2014 gaiyo.pdf 2) and new/saving /general/support/ 3) energy/japan energy 01.html 4) /017 s01 00.pdf 5) bldg/01.html [1] M. Inoue, T. Higuma, Y. Ito, N. Kushiro, and H. Kubota. Network architecture for home energy management system. IEEE Transactions on Consumer Electronics, Vol. 49, No. 3, pp , [2] K.Iatropoulos H.Doukas, K.D.Patlitzianas and John Psarras. Intelligent building energy management system using rule sets. Building and Environment, Vol. 42, No. 10, pp , [3] E.S.Lee and S.E.Selkowitz. The new york times headquarters daylighting mockup: Monitored performance of the daylighting control system. Energy and Buildings, Vol. 38, No. 7, pp , [4] S. Matta and S. M. Mahmud. An intelligent light control system for power saving. In IECON th Annual Conference on IEEE Industrial Electronics Society, pp , Nov [5] C.Suvagau A.D.Galasiu, G.R.Newsham and D.M.Sander. Energy saving lighting control systems for open-plan offices: A field study. LEUKOS, Vol. 4, pp. 7 29, [6] D.Manicria B.V.Neida and A.Tweed. An analysis of the energy and cost savings potential of occupancy sensors for commercial lighting systems. Journal of the Illuminating Engineering Society, Vol. 30, No. 2, pp , [7] G.P.Henze X.Guo, D.K.Tiller and C.E.Waters. The performance of occupancy-based lighting control systems: A review. Lighting Research & Technology, Vol. 42, p , [8] Vishal Garg and N.K.Bansal. Smart occupancy sensors to reduce energy consumption. Energy and Buildings, Vol. 32, No. 1, pp , [9] N.Li, G.Calis, and B.Becerik-Gerber. Measuring and monitoring occupancy with an rfid based system for demand-driven hvac operations. Automation in Construction, Vol. 24, pp , [10] T.Bretterklieber B.George, H.Zangl and G.Brasseur. A combined inductive-capacitive proximity sensor for seat occupancy detection. IEEE Transactions on Instrumentation and Measurement, Vol. 59, No. 5, [11] D.M.Gavrila. The visual analysis of human movement: A survey. Computer Vision and Image Understanding, Vol. 73, pp , [12] G.R.Newsham and Arsenault. A camera as a sensor for lighting and shading control. Lighting Research and Technology, Vol. 41, No. 2, pp , [13],,,,.., Vol. 56, No. 2, pp , [14],,. ]., Vol. 2012, No. 18, pp. 1 14, [15] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 05), Vol. 1, pp vol. 1, June [16] L.Zhao and C.E.Thorpe. Stereo- and neural networkbased pedestrian detection. IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 3, pp , Sep [17] H.He and E.A.Garcia. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 9, pp , [18] M.L.Shyu Y.Yan, M.Chen and S.C.Chen. Deep learning for imbalanced multimedia data classification IEEE International Symposium on Multimedia (ISM), pp , c 2017 Information Processing Society of Japan 8
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