Semi-automatic ground-truth trajectory extraction on image sequences

Download
2017
Karabıyık, Murat
In this thesis, offline semi-automatic ground-truth trajectory extraction technique is proposed that uses measurements of detector as basis. The unknown camera motion of the videos used throughout the thesis makes the problem even more challenging. The camera motion is estimated by using a novel method which uses a special Kalman filter. Background objects are discriminated from the targets and they are used to estimate the camera motion. Two different trackers are implemented to extract the ground-truth. Measurements of the detector are tracked by using Tracker-1. The tracks resulted from Tracker-1 are associated by using Tracker-2. The velocity difference between the target and the camera is used both for position predictions of Tracker-2. The user of the program gives the true target information for the first frame. The output of Tracker-2 gives the raw ground-truth and it is smoothed via Kalman smoother. The output of the Kalman smoother gives the ground-truth. Finally, an example tracker which is used in real time tracking problems is evaluated by comparing the ground-truth and measurements of the tracker which is evaluated. 

Suggestions

Background tracking of a video taken from a front camera of non maneuvering vehicle
Ünver, Önder; Demirekler, Mübeccel; Department of Electrical and Electronics Engineering (2014)
In this study, a novel background tracking technique is proposed that uses extended Kalman Gaussian mixture probability hypothesis density filtering approach. Since the background in a movie, taken from a front camera of a non maneuvering moving vehicle, exhibits a non-stationary nature, tracking the background is usually done by using pixel-wise comparisons in consequent frames. Besides, some methods use features of the background to track it. The proposed method uses the feature tracking approach. The fea...
Interacting multiple model probabilistic data association filter using random matrices for extended target tracking
Özpak, Ezgi; Orguner, Umut; Department of Electrical and Electronics Engineering (2018)
In this thesis, an Interacting Multiple Model – Probabilistic Data Association (IMM-PDA) filter for tracking extended targets using random matrices is proposed. Unlike the extended target trackers in the literature which use multiple alternative partitionings/clusterings of the set of measurements, the algorithm proposed here considers a single partitioning/clustering of the measurement data which makes it suitable for applications with low computational resources. When the IMM-PDA filter uses clustered mea...
Exact kalman filtering of respiratory motion
Çetinkaya, Mehmet; Erkmen, Aydan Müşerref (2018-10-01)
In this paper we propose a novel Exact Kalman Filter for state estimation of quasi-periodic signals such as respiratory motion. Nonlinear functions of interest are approximations as truncated Fourier series. Instead of relying on approximations provided by Extended Kalman Filter or Unscented Kalman Filter, our filter performs exact calculation of the mean and covariances of interest. We then compare, through simulations, the performance of our filter to the two. Our results show that the theoretically deriv...
Pseudo-Linear Kalman Filter for Attitude Estimation of a Spinning Nanosatellite
Söken, Halil Ersin; Kallio, Esa; Visala, Arto; Selkainaho, Jorma (null; 2017-06-09)
This paper presents the pseudo-linear estimation approach to the high-rate spinning small spacecraft attitude estimation problem. The sensor suit utilised in the presented approach uses gyro, magnetometer and sun-sensor measurements. The presented estimation technique has been designed particularly for the problem of attitude determination during the Aalto-1 nanosatellite's Plasma Brake Experiment (PBE). The design of the PBE demands the satellite to be spun up to 200 deg/s for deploying the tether by the u...
Electromagnetic target classification of small-scale aircraft modeled by conducting wire structures using a natural resonance based feature extraction technique
Ersoy, Mehmet Okan; Sayan, Gönül (2005-12-01)
The problem studied in this paper is the design of an electromagnetic target classifier for small-scale aircraft targets by using a natural resonance based feature extraction technique supported by feature fusion. The aircraft targets are modeled by perfectly conducting straight thin wire structures and the electromagnetic fields back-scattered from targets are numerically generated. This technique uses the Wigner-Ville distribution (WD) and the principal component analysis (PCA). The technique is applied t...
Citation Formats
M. Karabıyık, “Semi-automatic ground-truth trajectory extraction on image sequences,” M.S. - Master of Science, Middle East Technical University, 2017.