Coverage Estimation in Heterogeneous Visual Sensor Networks Mahmut Karakaya and Hairong Qi Advanced Imaging & Collaborative Information Processing Laboratory Electrical Engineering and Computer Science University of Tennessee, Knoxville Int. Conf. on Distributed Computing in Sensor Systems May 16 th, 2012

Multi-Camera Systems vs. VSN  Multi-camera system applications ranging from security monitoring to surveillance.  Deployment of many expensive and high-resolution cameras in large areas.  Not scalable and subjective to decisions of human operators, unaffordable to deploy many cameras –high cost of installation and system maintenance. 2 * Photo Courtesy of www. mobese.iem.gov.tr, of Rowe et al. (2007  Visual Sensor Platforms -Small size and low cost. -Imaging, on-board processing, wireless communication.  Collaboration in sensor networks is necessary -To compensate for the limitations of each node, -To improve the accuracy and robustness. CMUcam MSP

3  Huge data volume of camera –Limited energy source –Low-bandwidth communication –Local processing vs. computational cost  Directional sensing characteristic –Limited Field of View (FOV) –FOV < 180 o.  Visual occlusion –Capture a target only when –in the field of view –no other occluding targets –Not possible to cover all targets in a crowded environment by a single camera. Challenges in VSNs [1] Estrin, 2002

4 Target Detection Models Traditional: Intersections of the back-projected 2D visual cones of the targets.  Resolving on existence information  Focused on: How many sensors detects target existence at a single grid point. Progressive Certainty Map: Union of the non-occupied areas in the 2D visual hull.  Resolving on non-existence information  Focused on: How many sensors declares target non- existence at a single grid point.

Coverage in Sensor Networks 5 * Figure courtesy of Huang and Tseng (2003) In Scalar Sensor Networks: Based on the total number of nodes that captures an arbitrary target within their circular sensing range. In Visual Sensor Networks: More challenging because of unique features of cameras Directional sensing characteristic Presence of visual occlusion

Visual Coverage Estimation To reach a desired visual coverage, camera positions may not be predetermined due to –Random deployment –Impractical to manipulate sensor locations. 6 Sensor related parameters should be decided before deployment. –Number of sensors, sensing range, field of view. Visual coverage in a crowded area depending on –Sensor density and deployment –Target density and distribution.

7 Visual Coverage Estimation Occupancy Map vs. Certainty Map Occupancy Map Based Certainty Map Based

8 Occupancy Map based Coverage Estimation

Visual Coverage Estimation without Visual Occlusion 9

10 Visual Coverage Estimation without Visual Occlusion (cont.) The number of combinations of k- node subset from a j-node set The probability of not facing the node towards the center of A.

Visual Coverage Estimation in Heterogeneous VSNs In more realistic scenario: –Heterogeneous visual sensor deployment, –Heterogeneous target existence 11 Heterogeneous Visual Sensors Nodes Heterogeneous Targets

12 Introduction Collaborative Target Fault Tolerance, Detection Visual Coverage Experimental Conclusion Localization and Correction Estimation Results Heterogeneous Visual Sensor Deployment The probability that a detectability area contains exactly i many Type I sensor nodes and m of them can cover the corresponding grid point The probability that a detectability area contains exactly j- i many Type II sensor nodes and k- m of them can cover the corresponding grid point

13 Heterogeneous Sensor Deployment without Visual Occlusions The probability that a detectability area A 1 contains exactly i many Type I sensors nodes from a Poisson process The number of combinations The probability of that m many Type I sensor facing towards the center of A 1 The probability that A 2 contains exactly j- i many Type II nodes The probability of that k- m many Type II sensor facing towards the center of A 2

14 Visual Coverage Estimation with Visual Occlusion The node is located in the circle, Oriented towards the grid point, All targets be outside of the occlusion zone between the grid point and the sensor node.

15 Heterogeneous Sensor Deployment with Visual Occlusions

16 Heterogeneous Sensor Deployment with Visual Occlusions

17 Heterogeneous Target Existence

18 The probability that a detectability area A contains exactly j many sensors nodes from a Poisson process with sensor density λ s The number of combinations Heterogeneous Target Existence

Experiments for Visual Coverage Estimation Simulation setup: 40mx40m area, 10 targets, r=0.5m-2m, 30 sensor nodes, ρ=10m-15m and FOV=45 o -60 o. Two sets of experiment to compare the simulation results and theoretical values to validate the theoretical derivation of visual coverage probability. Visual Coverage Estimation without/with boundary effect The effects of two groups of parameters Sensor node related parameters Number of Sensors, Sensor Range, Angle of View Target related parameters. Number of Targets, Target radius Minimum Sensor Density Estimation 19

Effect of Sensor Related Parameters Comparison of theoretical values and simulation results corresponding to sensor node related parameters, Different numbers of sensor nodes, Different sensing range, Different angle of views. 20

Effect of Target Related Parameters 21 Comparison of theoretical values and simulation results corresponding to target related parameters, Different numbers of targets, Different sensing target radius.

Conclusion A closed form solution for the visual coverage estimation problem in the presence of visual occlusions among crowded targets in a VSN. Deployment of sensors follows a stationary Poisson point process. Derivation of the visual coverage estimation possible by modeling the target detection algorithm based on the target non- existence information. Heterogeneous sensor nodes or heterogeneous targets are likely to appear in the sensing field. Comparison of the simulation results and the theoretical values, Validate of the proposed close form solution of visual coverage estimation Show effectiveness of our model to be deployed in practical scenarios. 22

Thank you 23

Boundary Effect 24