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

Sensor platforms

Cameras

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 energy-efficiency

• 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 along which the centroid of an object lies

v

System Design—Object LocalizationInvolves 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

~470 J

~2900 J

(SensEye)

(Single Tier)

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

Thank you!

Power Management

• wake-on-wireless – Separation of the control channel and the

data channel– Incoming radio signal to wake up power-off

devices

• Turducken– Multi-tier structure that uses a lower tier to

wake up a higher tier

Multimedia Sensor Network

• Panoptes– Video-based sensor network– Single-tier, similar to tier 2 in SensEye– Incorporates compression, buffering and

filtering (can be used by tier 2)