Sage Journals: Discover world-class research

Abstract

In this paper we consider the problem of planning sensor observations for a network of overhead sensors which will resolve ambiguities in the output of a horizontal sensor network. More specifically, we address the problem of counting the number of objects detected by the horizontal sensor network, using the overhead network to aim at specific areas to improve the count. We consider several different overhead sensor models. The main theme of our results is that, even though observation planning is intractable for such a network, a simple, greedy algorithm for controlling the overhead sensors guarantees performance with bounded and reasonable suboptimality. Our results are very general and make few assumptions about the specific sensors used.

Keywords

sensor networks approximation algorithms combinatorial optimization.

Get full access to this article

View all access options for this article.

References

Berry, P. , Pontecorvo, C. and Fogg, D. ( 2003). Optimal employment of space surveillance resources for maritime target tracking and re-acquisition. International Conference on Information Fusion, pp. 719-725.

Chrobak, M. and Payne, T. ( 1989). A linear-time algorithm for drawing a planar graph on a grid. Information Processing Letters, 54: 241-246.

Darrell, T. , Gordon, G. , Woodfill, J. , Baker, H. and Harville, M. ( 1998). Robust real-time people tracking in open environments using integrated stereo, color and face detection. IEEE Workshop on Visual Surveillance.

Fowler, R.J. , Paterson, M.S. and Tanimoto, S.L. ( 1980). Optimal packing and covering in the plane are NP-complete . Information Processing Letters, 12: 133-137.

Graham, R. ( 1972). An efficient algorithm for determining the convex hull of a finite planar set. Information Processing Letters, 1: 132-133.

Haritaoglu, I. , Harwood, D. and Davis, L. ( 1998). W4S: A realime system for detecting and tracking people in 2 1/2D. Proceedings of ECCV.

He, Y. and Chong, E. ( 2006). Sensor scheduling for target tracking: A Monte Carlo sampling approach. Digital Signal Processing, 16: 533-545.

Isard, M. and MacCormick, J. ( 2001). BraMBLe: A Bayesian multiple-blob tracker. Proceedings IEEE ICCV, Vol. 2, pp. 34-41.

Larocque, J. , Reilly, J. and Ng, W. ( 2002). Particle filters for tracking an unknown number of sources . IEEE Transactions on Signal Processing, 50: 2926-2937.

10.

Laurentini, A. ( 1994). The visual hull concept for silhouette-based image understanding . IEEE Transactions on Pattern Analysis and Machine Intelligence , 16: 150-162.

11.

Matusik, W. , Buehler, C. and McMillan, L. ( 2001). Polyhedral visual hulls for real-time rendering. Eurographics Workshop on Rendering.

12.

Nemhauser, G. , Wolsley, L. and Fisher, M. ( 1978). An analysis of approximations for maximizing submodular set functions. Mathematical Programming, 14: 265-294.

13.

O’Rourke, J. and Supowit, K.J. ( 1983). Some NP-hard polygon decomposition problems. IEEE Transactions on Information Theory, 29: 181-190.

14.

Särkkä, S. , Vehtari, A. and Lampinen, J. ( 2007). Rao- Blackwellized particle filter for multiple target tracking. Information Fusion Journal, 8: 2-15.

15.

Yang, D. , Gonzalez-Banos, H. and Guibas, L. ( 2003). Counting people in crowds with a real-time network of simple image sensors. Proceedings IEEE ICCV.

16.

Zhao, T. and Nevatia, R. ( 2004). Tracking multiple humans in crowded environment. IEEE Computer Vision and Pattern Recognition, 2: 406-413.

Counting Objects with a Combination of Horizontal and Overhead Sensors

Abstract

Keywords

Get full access to this article

References