Solving Optimal Camera Placement Problems in IoT Using LH-RPSO

Abstract

With the increasing need for public security and intelligent life and the development of Internet of Things (IoT), the structure and application of vision sensor network are becoming more and more complex. It is no longer a system with simple static monitoring, but a complex system that can be used for intelligent processing, such as target localization, identification, tracking and so on. In order to accomplish various tasks efficiently, it is important to determine the deployment plan of camera network in advance. Many researches discretize the optimal camera placement problem into a binary integer programming (BIP) problem, which is NP-hard, and put forward some approximate solutions including greedy heuristics, semidefinite programming, simulated annealing, etc. In practice, however, camera parameters include both continuous values (location and orientation) and discrete values (camera type). To get a much more accurate result, we do not discretize the continuous camera parameters any more, on the contrary, we handle the continuous values in continuous domain directly. Meanwhile, a Latin Hypercube based Resampling Particle Swarm Optimization (LH-RPSO) algorithm is proposed to effectively solve the problem. To validate the proposed algorithm, we compared it with standard Particle Swarm Optimization (PSO) and Resampling Particle Swarm Optimization (RPSO). Simulation results for an outdoor planar regions illustrated the efficiency of the proposed algorithm.

Visual Sensor Networks for Indoor Real-Time Surveillance and Tracking of Multiple Targets

“…One further possible refinement could be to optimize the number, position, and type of cameras located inside the surveilled environment, for example by using the solutions proposed in [ 7 , 8 , 9 , 27 ]. This would allow starting from more advantageous PTZ parameter configurations as concerns the utility function maximization.…”

“…At the same time, we highlight that any assumption is stated as regards the specific camera’s placement within the environment: several existing algorithms allow determining the best sensor location [ 9 , 25 , 26 , 27 ].…”

“…The monitoring performance of the traditional VSNs, composed exclusively of fixed cameras, uniquely depends on the device number and positioning in the considered environment (see, e.g., [ 7 , 8 , 9 ]). On the contrary, when accounting for VSNs involving also PTZ cameras, several parameters can be dynamically selected and actively controlled in order to optimize the surveillance task’s fulfillment.…”

A Multiresolution Approach for Large Real-World Camera Placement Optimization Problems

“…Evolutionary algorithms like PSO have been extensively studied in camera placement optimization problems. Wang et al proposed two variants of PSO algorithm in [16] and [17], respectively. While the core algorithm remains same as the standard PSO, in RPSO, they use a weighted particle re-sampling scheme to maintain diversity in the particle population.…”

“…While the core algorithm remains same as the standard PSO, in RPSO, they use a weighted particle re-sampling scheme to maintain diversity in the particle population. Similarly, in LH-RPSO, [17], they replace the re-sampling strategy of RPSO with a new Latin Hypercube-based particle sampling strategy. Both their methods are claimed to improve solutions obtained by the standard PSO algorihtm.…”