Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction to object tracking
- 2 Filtering theory and non-maneuvering object tracking
- 3 Maneuvering object tracking
- 4 Single-object tracking in clutter
- 5 Single- and multiple-object tracking in clutter: object-existence-based approach
- 6 Multiple-object tracking in clutter: random-set-based approach
- 7 Bayesian smoothing algorithms for object tracking
- 8 Object tracking with time-delayed, out-of-sequence measurements
- 9 Practical object tracking
- Appendix A Mathematical and statistical preliminaries
- Appendix B Finite set statistics (FISST)
- Appendix C Pseudo-functions in object tracking
- References
- Index
6 - Multiple-object tracking in clutter: random-set-based approach
Published online by Cambridge University Press: 07 September 2011
- Frontmatter
- Contents
- Preface
- 1 Introduction to object tracking
- 2 Filtering theory and non-maneuvering object tracking
- 3 Maneuvering object tracking
- 4 Single-object tracking in clutter
- 5 Single- and multiple-object tracking in clutter: object-existence-based approach
- 6 Multiple-object tracking in clutter: random-set-based approach
- 7 Bayesian smoothing algorithms for object tracking
- 8 Object tracking with time-delayed, out-of-sequence measurements
- 9 Practical object tracking
- Appendix A Mathematical and statistical preliminaries
- Appendix B Finite set statistics (FISST)
- Appendix C Pseudo-functions in object tracking
- References
- Index
Summary
Typically, multiple-object tracking problems are handled by extending the singleobject tracking algorithms where each object is tracked as an isolated entity. The challenge comes when the targets are close by and there is ambiguity about the origin of the measurement, i.e., which measurements are from which track (in general). Using similar techniques of data association, multiple measurements are assigned to multiple objects (in general). However, such an extension of singleobject trackers to multiple-object trackers assumes that one knows the number of objects present in the surveillance space, which is not true.
This problem leads to some of the serious advances and methods of “data association” logic of these trackers. The data association step calculates the origin of the measurements in a probabilistic manner. It hypothesizes the measurement origin and calculates probabilities for each of the hypotheses. For example, a single-object tracking algorithm considers two hypotheses under measurement origin uncertainty – “the measurement is from an object of interest” or “the measurement is from clutter.” Such algorithms ignore the possibility of the measurements originating from other objects. This problem is partially solved by introducing the hypothesis “the measurement is from the ith (out of N) objects.” But setting the number of objects to a specific value is a limitation by itself. Moreover, this approach does not provide any measure for the validity of the number of objects. Multi-object trackers need to estimate the number of objects and their individual states jointly.
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- Chapter
- Information
- Fundamentals of Object Tracking , pp. 223 - 264Publisher: Cambridge University PressPrint publication year: 2011