Extended Object Tracking and Shape Classification

Recent extended target tracking algorithms provide reliable shape estimates while tracking objects. The estimated extent of the objects can also be used for online classification. In this work, we propose to use a Bayesian classifier to identify different objects based on their contour estimates during tracking. The proposed method uses the uncertainty information provided by the estimation covariance of the tracker.


Extended Target Tracking and Classification Using Neural Networks
Tuncer, Barkın; Kumru, Murat; Özkan, Emre (2019-01-01)
Extended target/object tracking (ETT) problem involves tracking objects which potentially generate multiple measurements at a single sensor scan. State-of-the-art ETT algorithms can efficiently exploit the available information in these measurements such that they can track the dynamic behaviour of objects and learn their shapes simultaneously. Once the shape estimate of an object is formed, it can naturally be utilized by high-level tasks such as classification of the object type. In this work, we propose ...
Extended target tracking using reduced rank gaussian processes
Özcan , Mustafa Buğra; Özkan, Emre; Department of Electrical and Electronics Engineering (2021-2-12)
Conventional tracking algorithms are predominantly based on point target assumption; however, this assumption is challenged as a result of the advents in sensor resolutions. Improvements on processors and rapid advances in sensor capabilities has enabled to the perception of target characteristics beyond the kinematics. Extended target tracking is the ability to learn target shapes that occupy multiple resolution cells and to track the motion of the target in a recursive framework. Gaussian process, a non-p...
Extended Target Tracking Using Gaussian Processes
Wahlström, Niklas; Özkan, Emre (2015-08-15)
In this paper, we propose using Gaussian processes to track an extended object or group of objects, that generates multiple measurements at each scan. The shape and the kinematics of the object are simultaneously estimated, and the shape is learned online via a Gaussian process. The proposed algorithm is capable of tracking different objects with different shapes within the same surveillance region. The shape of the object is expressed analytically, with well-defined confidence intervals, which can be used ...
Fully-Automatic Target Detection and Tracking for Real-Time, Airborne Imaging Applications
Alkanat, Tunc; Tunali, Emre; Oz, Sinan (2015-03-14)
In this study, an efficient, robust algorithm for automatic target detection and tracking is introduced. Procedure starts with a detection phase. Proposed method uses two alternatives for the detection phase, namely maximally stable extremal regions detector and Canny edge detector. After detection, regions of interest are evaluated and eliminated according to their compactness and effective saliency. The detection process is repeated for a predetermined number of pyramid levels where each level processes a...
Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation
Lundquist, Christian; Orguner, Umut; Gustafsson, Fredrik (Institute of Electrical and Electronics Engineers (IEEE), 2011-01-01)
This paper presents an extended target tracking framework which uses polynomials in order to model extended objects in the scene of interest from imagery sensor data. State-space models are proposed for the extended objects which enables the use of Kalman filters in tracking. Different methodologies of designing measurement equations are investigated. A general target tracking algorithm that utilizes a specific data association method for the extended targets is presented. The overall algorithm must always ...
Citation Formats
B. Tuncer, M. Kumru, A. A. Alatan, and E. Özkan, “Extended Object Tracking and Shape Classification,” 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36431.