SensEye: A Multi-Tier Camera Sensor Network by Purushottam Kulkarni, Deepak Ganesan, Prashant Shenoy, and Qifeng Lu
Presenters: Yen-Chia Chen and Iva...
SensEye: A Multi-Tier Camera Sensor Network by Purushottam Kulkarni, Deepak Ganesan, Prashant Shenoy, and Qifeng Lu
Presenters: Yen-Chia Chen and Ivan Pechenezhskiy EE225B (March 17, 2011)
Cameras and Sensor Platforms Cameras
Sensor platforms
Kulkarni et al, In Proc. of ACM NOSSDAV, pages 141–146, 2005.
Previous Work • Power Management – Wakeup-on-wireless & Turducken (always-on) • Multimedia Sensor Network – Panoptes (a video-based single-tier sensor network) • Sensor Placement – Solvable optimization problem • Video Surveillance – Techniques for target detection, classification, and tracking – Systems with central control unit
Motivation • Applications – Environmental monitoring – Ad-hoc surveillance • Constraints – No human interference – Battery-powered deployment
Multi-Tier Sensor Network • Single-Tier Network vs. Multi-Tier Network – reduces power consumption – achieves similar performance • Benefits: – Low cost – High coverage – High reliability – High functionality
SensEye: Multi-Tier Camera Network • Achieve low latencies without sacrificing energyefficiency • Tasks: object detection, recognition and tracking • Exploits redundancies in camera coverage (e.g. object localization)
General Design Principles • Map each task to the least powerful tier with sufficient resources • Exploit wakeup-on-demand • Exploit redundancy in coverage
System Design—Object Detection • Performed at the most energy-efficient tier (Tier 1) • Detection via frame differencing
• Randomized duty-cycling algorithm
System Design—Object Localization Calculation of the vector v along which the centroid of an object lies
System Design—Object Localization Involves two rotations and one translation
Transformation to the global coordinate frame
Triangulation
System Design—Inter-Tier Wakeup • Localization by tier 1 is used to decide which tier 2 nodes to wake up • Wakeup packet to node 2, similar to wake-on-wireless • Reduce the duration of wakeup: Tier 2 runs at bare minimum when suspended
System Design— Recognition and Tracking • Recognition algorithm executed at tier 2 • It is assumed any object recognition algorithm can be employed in SensEye • Tracking involves detection, localization, and inter-tier wakeup
Hardware Architecture Camera Sensors
Sensor Platforms
Hardware Architecture
• Tier 1: – lower-power camera sensors (Cyclop or CMUcam) – low-power sensor platform (Mote) • Tier 2: – webcams (Logitech) – sensor platform (Intel Stargate), low-power wakeup circuit (Mote) • Tier 3: – high-performance PZT camera and mini-ITX embedded PC (Sony)
Hardware Architecture
Software Architecture (Proposed)
Software Architecture (Implemented) • • • • •
CMUcam Frame Differentiator Mote-Level Detector Wakeup Mote High Resolution Object Detection and Recognition PTZ Controller
CMUcam Frame Differentiator • CMUcam image capture is triggered by Mote-Level Detector • Detection is achieved by differencing with reference background frame (non-zero areas correspond to object) • Two differencing modes: initial image (88x143 or 176x255) is converted to a 8x8 or 16x16 grid
Mote-Level Detector • • • • • • •
Sends initialization commands Sends sampling signal to CMUcam Gets the frame difference from CMUcam Decides whether an event occur Broadcasts a trigger to the higher tier if an even occur Sleeps, on no event detection Duty-cycles CMUcam
Wakeup Mote • Receives Triggers from the lower tier Motes • Computes the coordinates of the detected object • Decides whether to wakeup Stargate
High Resolution Object Detection and Recognition by Stargate • Frame differencing • Image smoothing • Obtaining an average value of the red, green and blue components of the object • Matching against a library of objects
Experimental Evaluation • Component Benchmarks – Latency and Energy Consumption – Localization Accuracy • SensEye vs. Single-Tier Network – Coverage – Energy Usage – Sensing Reliability – Sensitivity to System Parameters
Latency and Energy Consumption • Tier 1: – Cyclope
– CMUcam • Tier 2: – webcam
Latency and Energy Consumption • Tier 1: – Cyclope
– CMUcam • Tier 2: – webcam
4 sec
4.7 J
Localization Accuracy
Experimental Evaluation: Sensor Placement and Coverage wall 3m x 1.65m
• Object appearance time: 7 sec • Interval between appearance: 30 sec • Only one object at any time • 50 object appearances • Tier 1 Motes sampling period: 5 sec
Network Energy Usage (SensEye)
~470 J
(Single Tier)
~2900 J
Sensing Reliability • Single-tier system detected 45 out of the 50 objects • SensEye detected 42 (46 with the use of PZT)
Sensitivity to System Parameters
Conclusion • A well-design multi-tier camera sensor network might have significant benefits over a single-tier camera network • General principles for multi-tier sensor network design have been proposed • It has been experimentally demonstrated that a multi-tier network can achieve about an order of magnitude reduction in energy usage without sacrificing reliability