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Tracking and Data Fusion: A Handbook of Algorithms
The book as a comprehensive introduction (>1250 pages) to the field of MOT with a detailed discussion on numerous practical issues. The book is written by one of the world's experts in the field and provides numerous realistic examples using sensors such as radar, ultrasonic etc. The topics addressed by this monumental work include tracking of the maneuvering target, PDA-based methods, track-to-track fusion, tracking and association with attributes, measurement extraction for unresolved targets, sensor management etc.
The work provides an extensive review on state of the art methods for the problem of MOT. The paper consists of three main sections where correspondingly the methods of Joint Probabilistic Data Association (JPDA), Multiple Hypothesis Tracking (MHT) and the methods of RFS are discussed. For these three groups of the algorithms the key features are discussed and the extension methods are mentioned.
Fundamentals of Object Tracking
The work starts with a Bayesian solution for a generic object tracking and proceeds to a problem of MOT. First, the association-based methods such starting with a relatively simple JPDA and ending with advanced IMM-JITS. Additionally, newer FISST-based methods are introduced and explained as an alternative to classical association-based for the MOT problem. Finally, the reader is introduced to the concepts Out-of-Sequence Measurements (OOSM), where the tracking algorithm is designed to incorporate delayed or out-of-time order measurements correctly as seen from the processing system. The latter effect is considered of an extreme importance in modern tracking system which relies on the networked sensors interconnected with complex communication networks as well as due to the delays caused by the internal sensor processing.
The book provides a comprehensive introduction into the methods of Bayesian inference in robotics. The reader gets an easy-to-read introduction to the methods of both parametric and non-parametric filtering with an emphasis on motion and perception models as used in robotics applications. Additionally to classical estimation algorithms, an excellent introduction is given for the localization and mapping applications such as occupancy grid mapping and efficient implementations of Simultaneous Localization and Mapping (SLAM).
Taxonomy of Multiple Target Tracking Methods
The work presents a broad overview of the MTT algorithms as available before 2005 with a comparative analysis or the methods in terms of their processing structure, computational complexity and performance as well as association type used. The report concentrates on enumerative algorithms (the group of data association based methods) and does not consider newer FISST methods which got popularity after the work was published.
Extended Object Tracking: Introduction, Overview and Applications
The paper provides an excellent introduction on the state-of-the-art methods on extended object tracking. The work starts with a overview on the basic methods used to track a single extended object, while an extension of the methods for tracking multiple extended objects is provided in the second part of the paper. The overview paper has a tutorial structure with a number of important algorithms explained in a form of pseudocode.
A Consistent Metric for Performance Evaluation of Multi-Object Filters
The work addresses a problem of defining a miss-distance measure as a metric to assess the performance of a multi-object tracking algorithms. The authors introduce and explain in details a motivation behind a so-called Optimal Subpattern Assignment (OSPA) metric - a performance metric which is nowadays commonly accepted as one of the major Key Performance Indicators (KPI) for MOT algorithms.
A Random Finite Set Approach for Dynamic Occupancy Grid Maps
This dissertation presents a new concept that describes the dynamic grid mapping as an approximation of a random finite set (RFS) filter. It represents the environment as a Dempster-Shafer grid where each cell is either free or occupied. While the free mass is attached to a cell, the mass for being occupied is represented by a fixed-sized grid-wide set of particles, which can move between grid cells over time, thus allowing the representation of moving objects in the grid. The author further introduces the update of the grid cell masses by lidar measurements via a measurement grid and presents a parallelized implementation of the algorithm.
Fully Bayesian Vehicle Tracking Using Extended Object Models
The dissertation proposes an integral solution for the extended multi-object tracking problem. A particle-based implementation of the labelled multi-Bernoulli filter is used as environment representation with additional discrete distributions for the extends of the objects. The author introduces extended object models for radar, lidar and scene labeling data for updating the multi-object state in a congenial way. In particular, the radar model was learned from real measurement data instead of expert knowledge