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1 VSAM : y, yy y yy : (CMU) VSAM (Video Surveillance and Monitoring) 3D VSAM VSAM : Video Surveillance and Monitoring Project Hironobu FUJIYOSHI y, Takeo KANADE yy y Department of Computer Science, College of Engineering, Chubu University, yy The Robotics Institute, Carnegie Mellon University Abstract: The Video Surveillance and Monitoring (VSAM) team at Carnegie Mellon University (CMU) has developed an end-to-end, multi-camera surveillance system that allows a single human operator to monitor activities in a cluttered environment using a distributed network of active video sensors. Video understanding algorithms have been developed to automatically detect people and vehicles, seamlessly track themusing a network of cooperating active sensors, determine their 3D locations with respect to a geospatial site model, and present this information to a human operator who controls the system through a graphical user interface. The goal is to automatically collect and disseminate real-time information to improve the situational awareness of security providers and decision makers. The feasibility of real-time video surveillance has been demonstrated within a multi-camera testbed system developed on the campus of CMU. This paper presents an overview of the issues and algorithms involved in creating this semi-autonomous, multi-camera surveillance system. 1
2 DARPA (Defence Advanced Research Projects Agency) VSAM (Video Surveillance and Monitoring) [1] (CMU) 12 [2] VSAM 2 VSAM VSAM VSAM 1 3D 1 (CG) Individual Sensors "image space" Sensor 1 Detection Tracking Classification Activity Analysis Sensor 2 Sensor N Data Fusion Sensor Tasking Multi-Sensor System "scene space" geolocation calibration visibility analysis focus of attention Site Model 1: Scene Visualization graphical display simulation / planning User 2.2 VSAM (3D ) USGS (U.S.Geological Survey) DEM (Digital Elevation Map) 1 =30m 2 VSAM CMU DEM ( 2 ) GPS [3] 2(c) 2
3 Smith (a) 3D site model of CMU campus Wean PRB (b) three sensors used for cooperative surveillance Color Monochrome 50 Meters (a) placement of cameras (c) operator console (b) real image (c) synthsized image CMUPA CMUPA GUI OCU 2: SPUs Site Model Sensor Fusion DIS VIS 2.3 CMU 0:4km 2 12 ( 3(a) ) 3(d) SPUs (Sensor Processing Units), OCU (Operator Contorol Unit), GUI (map-based Graphical User Interface), VIS (Visualization nodes) SPU pan/tilt/zoom PC SPU OCU OCU SPU GUI [4] (d) schematic overview of testbed system architecture 3: [5] 4 2 ( ) 2 8 3
![83% 92% ID [2] 4: Layer 1 stopped object Layer 2 stopped object moving object 3.2 ffl ffl ffl VSAM 12 ( ) 3.2.1 1000 ( 2 = ) ( ), ( ),, 4 3 ID ( 4, 16, 4) 3.](/docs-images/93/111244035/images/4-0.jpg "2.2 ( ) 2000 f g FedEx (2 ) 1,2,3 (2 3=6 ) ( 2 ) (1 ) 11 k [6] 3.2.3 ( 1500 1000) 6 3 [7] k 5 90% 3.3 4")
4 83% 92% ID [2] 4: Layer 1 stopped object Layer 2 stopped object moving object 3.2 ffl ffl ffl VSAM 12 ( ) ( 2 = ) ( ), ( ),, 4 3 ID ( 4, 16, 4) ( ) 2000 f g FedEx (2 ) 1,2,3 (2 3=6 ) ( 2 ) (1 ) 11 k [6] ( ) 6 3 [7] k 5 90% 3.3 4
![0,end distance distance 0 i end border position distance d(i) centroid e DFT LPF a d Inverse DFT ^ d(i) b c "star" skeleton of the shape a b c d e (a) star skeleton process 0.125 [sec] 3.](/docs-images/93/111244035/images/5-0.jpg "3.1 5: [8] [9] ( 6 ) 3.3.2 ( ) ( ) (Appearing, Moving, Stopped, Disappearing), (Near, MovingAwayFrom, MovingTowards, NoInteraction) [10] 6 5.3 [rad] θ [rad] φ 1 0.5 0-0.")
5 0,end distance distance 0 i end border position distance d(i) centroid e DFT LPF a d Inverse DFT ^ d(i) b c "star" skeleton of the shape a b c d e (a) star skeleton process [sec] : [8] [9] ( 6 ) ( ) ( ) (Appearing, Moving, Stopped, Disappearing), (Near, MovingAwayFrom, MovingTowards, NoInteraction) [10] [rad] θ [rad] φ (b) skeleton motion of a walking person (c) skeleton motion of a running person (d) leg angle θ of a walking person (f) torso angle φ of a walking person [rad] θ [rad] φ (e) leg angle θ of a running person (g) torso angle φ of a running person 6: (1) human entered a vehicle. (2) human exited a vehicle. (3) human entered a building. (4) human exited a building. (5) avehicle parked. (6) human rendevouz VSAM 5
6 12 13 Ray: (X0,Y0,Z0) + k(u,v,w) Vertical Projection Ray: (X0,Y0) + k(u,v) 11 X 9 10 X Elev( X0+kU, Y0+kV ) > Z0 + kw 2 X0, Y0, Z0 0 X0, Y0 WWW 5.1 GUI SPU 2 GUI ( 8 ) ( ) FOV (Field of view) GUI 7: ( ) 3 (DEM) 3 ( 7 ) Leica 65m 0.6m 5 (, 3D ) 2 GUI, CG 3D 8: GUI 5.2 CG 3D CG CG 5.3 WWW Human 6
7 6 まとめ 本稿では 複数のカメラを用いたビデオ監視 システムのプロジェクト VSAM について 動 画像理解技術と監視結果の表示方法を中心に述 べた VSAM の特徴は 動画像理解技術とサ イトモデルを組み合わせることにより広域の実 時間監視が可能となる点である VSAM プロ ジェクトの技術課題や本稿では省略した複数の センサによる協調動作等については 参考文献 や Web site( を参照いただきたい CMU VSAM Project: Robert T. 謝辞 図 9: CG による動的シーンの再現 Collins, Alan J. Lipton, David Duggins, Osamu Hasegawa, Nobuyoshi Enomoto の諸氏に は WWW を介してサーバ上のデータベースを 参照することで 監視場所で起こったアクティ ビティの要約を確認することができる WEB サーバは ユーザ (WEB ブラウザ) の要求に対 して CGI(Common Gateway Interface) により データベースからアクティビティの要約結果を 自動生成し表示する [11] 深く感謝する 参考文献 [1] VSAM: \Section I, video surveillance and monitoring", Proc. of the 1998 DARPA Image Understanding Workshop, Vol.1, pp. 1{400 (Nov. 1998). WWW によるアクティビティの要約結果の 表示例を図 10 に示す (a) は アクティビティ [2] R. Collins, A. Lipton, H. Fujiyoshi and T. Kanade: \Algorithms for cooperative multi-sensor surveillance", Proc. of IEEE Special Issue on "Video Communications, Processing and Understanding for Third Generation Surveillance Systems", (Oct. 2001). レポートの例である アクティビティレポー トは \A Human got out of a Vehicle", \A Vehicle parked" 等のイベント結果を時刻順に 表示する もし ユーザがアクティビティに関 与した物体の詳細を知りたいとき 物体の識別 タイプや色等の情報とその画像をハイパーテキ ストリンクにより表示することができる [3] R. Collins and Y. Tsin: \Calibration of an outdoor active camera system", Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 528{ 534 (Jun. 1999). (b) はオブジェクトリポートの例である オ ブジェクトリポートは システムによって検出 された全ての物体の一覧を表示する 観測さ れた物体数が多いとき ユーザは全ての物体を 確認することが不可能であるため 識別タイプ 毎に表示することができる また ユーザがあ る特定の物体をマウスクリックにより指定する と システムはデータベースに登録された他の 物体との類似度を識別タイプと色情報から計算 し 類似度の高い順に表示する これにより 監視領域内の別の場所や異なる時間帯に観測さ れた同一対象物体の長時間の行動を推定するこ とができる [4] A. Lipton, H. Fujiyoshi and R. Patil: \Moving target detection and classi cation from real-time video", Proc. of the 1998 Workshop on Applications of Computer Vision, pp. 8{14 (Oct. 1998). [5] 藤吉弘亘, 金出武雄: \複数物体の重なりを 理解するレイヤー型検出法", 第 7 回画像 センシングシンポジウム論文集, pp. 369{ 374 (2001-6). 7
![click more detail (a) Activity report click search (b) Object report 10: WWW [6], : ", 7, pp.](/docs-images/93/111244035/images/8-0.jpg "221 226 (2001-6). [7] Y. Ohta and T.")
8 click more detail (a) Activity report click search (b) Object report 10: WWW [6], : ", 7, pp (2001-6). [7] Y. Ohta and T. Kanade: Color information for region segmentation", Computer Graphics and Image Processing, Vol. 13, No. 3, pp (1980). [8] J.K. Aggarwal and Q. Cai. Human motion analysis: A review", Computer Vision and Image Understanding, Vol. 73 No. 3, pp (Mar. 1999). [9] H. Fujiyoshi and A. Lipton: Real-time human motion analysis by image skeletonization", Proc. of the 1998 Workshop on Applications of Computer Vision, pp (Oct. 1998). [10] N. Enomoto, T. Kanade, H. Fujiyoshi and O. Hasegawa. A method for monitoring activities of multiple objects by using stochastic model", IEICE Transactions on Information and Systems, Vol. 84-D No. 12 (Dec. 2001). [11],,, : WWW ", 7, pp (2001-6). 8
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