Efficient Bayesian track-before-detect

2006-10-11
Tekinalp, Serhat
Alatan, Abdullah Aydın
This paper presents a novel Bayesian recursive track-before-detect (TBD) algorithm for detection and tracking of dim targets in optical image sequences. The algorithm eliminates the need for storing past observations by recursively incorporating new data acquired through sensor to the existing information. It calculates the likelihood ratio for optimal detection and estimates target state simultaneously. The technique does not require velocity-matched filtering and hence, it is capable of detecting any target moving in any direction. The algorithm is tested with both synthetic and real video sequences, and is shown to be capable of performing sufficiently well for very low signal-to-noise ratio situations.

Suggestions

HYPERSPECTRAL UNMIXING BASED VEGETATION DETECTION WITH SEGMENTATION
Özdemir, Okan Bilge; Soydan, Hilal; Çetin, Yasemin; Duzgun, Sebnem (2016-07-15)
This paper presents a vegetation detection application with semi-supervised target detection using hyperspectral unmixing and segmentation algorithms. The method firstly compares the known target spectral signature from a generic source such as a spectral library with each pixel of hyperspectral data cube employing Spectral Angle Mapper (SAM) algorithm. The pixel(s) with the best match are assumed to be the most likely target vegetation locations. The regions around these potential target locations are furt...
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...
A PHD Filter for Tracking Multiple Extended Targets Using Random Matrices
Granstrom, Karl; Orguner, Umut (2012-11-01)
This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets' extensions are modeled as random matrices. For this purpose, the random matrix framework developed recently by Koch et al. is adapted into the extended target PHD framework, resulting in the Gaussian inverse Wishart PHD (GIW-PHD) filter. A suitable multiple target likelihood is derived, and the main filter recursion is present...
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 ...
Extended Target Tracking using a Gaussian-Mixture PHD Filter
Granstrom, Karl; Lundquist, Christian; Orguner, Umut (2012-10-01)
This paper presents a Gaussian-mixture (GM) implementation of the probability hypothesis density (PHD) filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experiments with real laser data, and the advantage of the filter is illustrated. S...
Citation Formats
S. Tekinalp and A. A. Alatan, “Efficient Bayesian track-before-detect,” 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43924.