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Dim Object Tracking in Cluttered Image Sequences

Ahmadi, Kaveh, ahmadi
http://orcid.org/0000-0001-9460-892X
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470147209

Abstract Details

2016, Doctor of Philosophy, University of Toledo, Engineering.
This research is aimed at developing efficient dim object tracking techniques in cluttered image sequences. In this dissertation, a number of new techniques are presented for image enhancement, super resolution (SR), dim object tracking, and multi-sensor object tracking. Cluttered images are impaired by noise. To deal with a mixed Gaussian and impulse noise in the image, a novel sparse coding super resolution is developed. The sparse coding has recently become a widely used tool in signal and image processing. The sparse linear combination of elements from an appropriately chosen over-complete dictionary can represent many signal patches. The proposed SR is composed of a Genetic Algorithm (GA) search step to find the optimum match from low resolution dictionary. By using GA, the proposed SR is capable of efficiently up-sampling the low resolution images while preserving the image details. Dim object tracking in a heavy clutter environment is a theoretical and technological challenge in the field of image processing. For a small dim object, conventional tracking methods fail for the lack of geometrical information. Multiple Hypotheses Testing (MHT) is one of the generally accepted methods in target tracking systems. However, processing a tree structure with a significant number of branches in MHT has been a challenging issue. Tracking high-speed objects with traditional MHT requires some presumptions which limit the capabilities of these methods. In this dissertation, a hierarchal tracking system in two levels is presented to solve this problem. For each point in the lower-level, a Multi Objective Particle Swarm Optimization (MOPSO) technique is applied to a group of consecutive frames in order to reduce the number of branches in each tracking tree. Thus, an optimum track for each moving object is obtained in a group of frames. In the upper-level, an iterative process is used to connect the matching optimum tracks of the consecutive frames based on the spatial information and fitness values. Another problem of dim object tracking is background subtraction which is difficult due to noisy environment. This dissertation presents a novel algorithm for detecting and tracking small dim targets in Infrared (IR) image sequences with low Signal to Noise Ratio (SNR) based on the frequency and spatial domain information. Using a Dual-Tree Complex Wavelet Transform (DT-CWT), a Constant False Alarm Rate (CFAR) detector is applied in the frequency domain to find potential positions of objects in a frame. Following this step, a Support Vector Machine (SVM) classification is applied to accept or reject each potential point based on the spatial domain information of the frame. The combination of the frequency and spatial domain information demonstrates the high efficiency and accuracy of the proposed method which is supported by the experimental results. One of the important tools applied in this dissertation is Particle Filter (PF). The PF, a nonparametric implementation of the Bayes filter, is commonly used to estimate the state of a dynamic non-linear non-Gaussian system. Despite PF’s successful applications, it suffers from sample impoverishment in real world applications. Most of the recent PF based techniques try to improve the functionality of the PF through evolutionary algorithms in the cases of unexpected changes in the system states. However, they have not addressed the discontinuity of observation which is unpreventable in the real world. This dissertation incorporates a recently developed social-spider optimization technique into PF to overcome the drawback of previous methods in these cases. The problem of object tracking using multi-sensor data is a theoretical and technological challenge in the field of image processing which is presented as the final algorithm in this dissertation. Most of the conventional multi-sensor methods fail to track small dim objects in a cluttered background due to the lack of geometrical target information and unexpected large discontinuities in the measurement data. In this dissertation, a multi-sensor Swarm Intelligence Particle Filter (SIPF) is proposed in an environment covered by a set of multiple calibrated sensors with overlapping field of views. The proposed hierarchical method is divided into two levels. In the lower-level, SIPF is applied to locate the targets in each sensor based on the prior information. Each sensor reports the target position and its related fitness value to a dynamically selected central sensor. In the upper-level, the central sensor finds the best of the reported position for each target and broadcasts its position to all sensors at the lower level as the actual position of the target. Experimental results show this method is able to utilize multi-sensor data to produce highly accurate tracks in noisy datasets even in the case of large jumps or discontinuous observations well beyond the conventional tracking methods.
Ezzatollah Salari (Committee Chair)
Kim Junghwan (Committee Member)
Jamali Mohsin (Committee Member)
Carvalho Jackson (Committee Member)
Eddie Yein Juin Chou (Committee Member)
165 p.
Computer Engineering; Computer Science
Dim Object Tracking; Particle Filter; Particle Swarm Optimization; Multiple Object Tracking; Multi Sensor Object Tracking; Super Resolution

Recommended Citations

Citations

  • Ahmadi, ahmadi, K. (2016). Dim Object Tracking in Cluttered Image Sequences [Doctoral dissertation, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470147209

    APA Style (7th edition)

  • Ahmadi, ahmadi, Kaveh. Dim Object Tracking in Cluttered Image Sequences. 2016. University of Toledo, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470147209.

    MLA Style (8th edition)

  • Ahmadi, ahmadi, Kaveh. "Dim Object Tracking in Cluttered Image Sequences." Doctoral dissertation, University of Toledo, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470147209

    Chicago Manual of Style (17th edition)

toledo1470147209
1,083
© 2016, some rights reserved.Creative Commons License Dim Object Tracking in Cluttered Image Sequences by Kaveh Ahmadi ahmadi is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Toledo and OhioLINK.