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Greg Okopal

Senior Engineer

Email

okopal@apl.washington.edu

Phone

206-616-6775

Education

B.S. Computer Engineering, Villanova University, 2002

M.S. Electrical Engineering, University of Pittsburgh, 2006

Ph.D. Electrical Engineering, University of Pittsburgh, 2009

Publications

2000-present and while at APL-UW

Robust human tracking based on DPM constrained multiple-kernel from a moving camera

Hou, L., W. Wan, K.-H. Lee, J.-N. Hwang, G. Okopal, and J. Pitton, "Robust human tracking based on DPM constrained multiple-kernel from a moving camera," J. Sign. Process. Syst., 86, 27-39, doi:10.1007/s11265-015-1097-y, 2017

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1 Jan 2017

In this paper, we attempt to solve the challenging task of precise and robust human tracking from a moving camera. We propose an innovative human tracking approach, which efficiently integrates the deformable part model (DPM) into multiple-kernel tracking from a moving camera. The proposed approach consists of a two-stage tracking procedure. For each frame, we first iteratively mean-shift several spatially weighted color histograms, called kernels, from the current frame to the next frame. Each kernel corresponds to a part model of a DPM-detected human. In the second step, conditioned on the tracking results of these kernels on the later frame, we then iteratively mean-shift the part models on that frame. The part models are represented by histogram of gradient (HOG) features, and the deformation cost of each part model provided by the trained DPM detector is used to constrain the movement of each detected body part from the first step. The proposed approach takes advantage of not only low computation owing to the kernel-based tracking, but also robustness of the DPM detector without the need of laborious human detection for each frame. Experimental results have shown that the proposed approach makes it possible to successfully track humans robustly with high accuracy under different scenarios from a moving camera.

Ground-moving-platform-based human tracking using visual SLAM and constrained multiple kernels

Lee, K.-H., J.-N. Hwang, G. Okopal, and J. Pitton, "Ground-moving-platform-based human tracking using visual SLAM and constrained multiple kernels," IEEE Trans. Intel. Transport. Syst., 17, 3602-3612, doi:10.1109/TITS.2016.2557763, 2016.

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1 Dec 2016

This paper proposes a robust ground-moving-platform-based human tracking system, which effectively integrates visual simultaneous localization and mapping (V-SLAM), human detection, ground plane estimation, and kernel-based tracking techniques. The proposed system systematically detects humans from recorded video frames of a moving camera and tracks the humans in the V-SLAM-inferred 3-D space via a tracking-by-detection scheme. To efficiently associate the detected human frame by frame, we propose a novel human tracking framework, combining the constrained-multiple-kernel tracking and the estimated 3-D information (depth), to globally optimize the data association between consecutive frames. By taking advantage of the appearance model and 3-D information, the proposed system not only achieves high effectiveness but also well handles occlusion in the tracking. Experimental results show the favorable performance of the proposed system, which efficiently tracks humans in a camera equipped on a ground-moving platform such as a dash camera and an unmanned ground vehicle.

Speech analysis with the strong uncorrelating transform

Okopal, G., S. Wisdom, and L. Atlas, "Speech analysis with the strong uncorrelating transform," IEEE/ACM Trans. Audio, Speech, Language Process., 23, 1858-1868, doi:10.1109/TASLP.2015.2456426, 2015.

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1 Nov 2015

The strong uncorrelating transform (SUT) provides estimates of independent components from linear mixtures using only second-order information, provided that the components have unique circularity coefficients. We propose a processing framework for generating complex-valued subbands from real-valued mixtures of speech and noise where the objective is to control the likely values of the sample circularity coefficients of the underlying speech and noise components in each subband. We show how several processing parameters affect the noncircularity of speech-like and noise components in the subband, ultimately informing parameter choices that allow for estimation of each of the components in a subband using the SUT. Additionally, because the speech and noise components will have unique sample circularity coefficients, this statistic can be used to identify time-frequency regions that contain voiced speech. We give an example of the recovery of the circularity coefficients of a real speech signal from a two-channel noisy mixture at -25 dB SNR, which demonstrates how the estimates of noncircularity can reveal the time-frequency structure of a speech signal in very high levels of noise. Finally, we present the results of a voice activity detection (VAD) experiment showing that two new circularity-based statistics, one of which is derived from the SUT processing, can achieve improved performance over state-of-the-art VADs in real-world recordings of noise.

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Deformable multiple-kernel based human tracking using a moving camera

Hou, L., W. Wan, K.-H. Lee, J.-N. Hwang, G. Okopal, and J. Pitton, "Deformable multiple-kernel based human tracking using a moving camera," Proc., 2015 IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP), 19-24 April, South Brisbane, Queensland, 2249-2253, dos:1109/ICASSP.2015.7178371 (IEEE, 2015).

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19 Apr 2015

In this paper, we propose an innovative human tracking algorithm, which efficiently integrates the deformable part model (DPM) into the multiple-kernel based tracking using a moving camera. By representing each part model of a DPM detected human as a kernel, the proposed algorithm iteratively mean-shift the kernels (i.e., part models) based on color appearance and histogram of gradient (HOG) features. More specifically, the color appearance features, in terms of kernel histogram, are used for tracking each body part from one frame to the next, the deformation cost provided by DPM detector is further used to constrain the movement of each body kernel based on the HOG features. The proposed deformable multiple-kernel (DMK) tracking algorithm takes advantage of not only low computation owing to the kernel-based tracking, but also robustness of the DPM detector. Experimental results have shown the favorable performance of the proposed algorithm, which can successfully track human using a moving camera more accurately under different scenarios.

Voice activity detection using subband noncircularity

Wisdom, S., G. Okopal, L. Atlas, and J. Pitton, "Voice activity detection using subband noncircularity," in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 19-24 April, South Brisbane, Queensland, 4505-4509, doi:10.1109/ICASSP.2015.7178823 (IEEE, 2015).

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19 Apr 2015

Many voice activity detection (VAD) systems use the magnitude of complex-valued spectral representations. However, using only the magnitude often does not fully characterize the statistical behavior of the complex values. We present two novel methods for performing VAD on single- and dual-channel audio that do completely account for the second-order statistical behavior of complex data. Our methods exploit the second-order noncircularity (also known as impropriety) of complex subbands of speech and noise. Since speech tends to be more improper than noise, higher impropriety suggests speech activity. Our single-channel method is blind in the sense that it is unsupervised and, unlike many VAD systems, does not rely on non-speech periods for noise parameter estimation. Our methods achieve improved performance over other state-of-the-art magnitude-based VADs on the QUT-NOISE-TIMIT corpus, which indicates that impropriety is a compelling new feature for voice activity detection.

Driving recorder based on-road pedestrian tracking using visual SLAM and constrained multiple-kernel

Lee, K.-H., J.-N. Hwang, G. Okopal, and J. Pitton, "Driving recorder based on-road pedestrian tracking using visual SLAM and constrained multiple-kernel," in 2014 IEEE 17th International Conference on Intelligent Transportation System (ITSC), 8-11 October, Qingdao, 2629-2635, doi:10.1109/ITSC.2014.6958111 (IEEE, 2014).

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8 Oct 2014

This paper proposes a robust driving recorder based on-road pedestrian tracking system, which effectively integrates Visual Simultaneous Localization And Mapping (V-SLAM), pedestrian detection, ground plane estimation, and kernel-based tracking techniques. The proposed system systematically detects the pedestrians from recorded video frames and tracks the pedestrians in the V-SLAM inferred 3-D space via a tracking-by-detection scheme. In order to efficiently associate the detected pedestrian frame-by-frame, we propose a novel tracking framework, combining the Constrained Multiple-Kernel (CMK) tracking and the estimated 3-D (depth) information, to globally optimize the data association between consecutive frames. By taking advantage of the appearance model and 3-D information, the proposed system not only achieves high effectiveness but also well handles occlusion in the tracking. Experimental results show the favorable performance of the proposed system which efficiently tracks on-road pedestrian in a moving camera equipped on a driving vehicle.

Tracking drifting surface objects with aerial infrared and electro-optical sensors

Krout, D.W., G. Okopal, A. Jessup, and E. Hanusa, "Tracking drifting surface objects with aerial infrared and electro-optical sensors," Proc., MTS/IEEE Oceans 2012, 14-19 October, Hampton Roads, VA, doi:10.1109/OCEANS.2012.6404804 (MTS/IEEE, 2012).

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14 Oct 2012

Recently, researchers at the Applied Physics Laboratory at the University of Washington collected a unique dataset by suspending two cameras, one infrared and one electro-optical, from a balloon. This apparatus was then used to image objects drifting on the surface of Lake Washington. The authors took that data and built a processing stream to track the movements of those drifting surface objects.

Object tracking with imaging sonar

Krout, D.W., W. Kooiman, G. Okopal, and E. Hanusa, "Object tracking with imaging sonar," Proc., 15th International Conference on Information Fusion, FUSION 2012, 2400-2405 (IEEE, 2012).

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30 Aug 2012

Recently a data set was collected using an imaging sonar of a non-stationary underwater object. This paper presents the image processing algorithms as well as the tracking algorithms used to take the imaging sonar data and track a non-stationary underwater extended object. The tracking results will be presented in a geo-referenced image frame with the use of GPS and inertial sensors. Future work with this data set will include feature extraction and object classification using the imaging sonar data.

Application of low-frequency methods for estimating object size

McLaughlin, J., B. Hamschin, and G. Okopal, "Application of low-frequency methods for estimating object size," J. Acoust. Soc. Am., 129, 2663, doi:10.1121/1.3588906, 2011.

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1 Apr 2011

Classification of submerged objects has traditionally been performed using high frequency sonars and imaging techniques. While this permits fine matching of target templates to images acquired in the field, HF methods are necessarily limited in range due to absorption of sound by the water. LF sonars, while offering increased detection range, come with some significant challenges related to the limited bandwidth available. Nonetheless, we show that it is feasible to estimate object size using nonimaging techniques. There are a number of low-frequency phenomena that can be exploited to this end. Among these are edge diffraction in which sharply angled facets of objects ("edges") act like independent, radiating point sources, and helical waves, which can be set up in cylindrical objects. We show that with appropriate postprocessing of these returns, object edges can be localized thus allowing object extent to be assessed. In this paper, we describe our processing system, and then give results when this system is applied to over 40 sequences of returns from a rail system. In each sequence, a single solid, proud cylinder is insonified, and our system reports an estimate of cylinder length and radius. Histograms of these estimates cluster roughly around the true values.

Propagation-invariant classification of sounds in channels with dispersion and absorption

Okopal, G., and P.J. Loughlin, "Propagation-invariant classification of sounds in channels with dispersion and absorption," J. Acoust. Soc. Am., 128, 2888-2897, doi:10.1121/1.3493420, 2010.

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1 Nov 2010

In a previous paper [ G. Okopal et al., J. Acoust. Soc. Am. 123, 832–841 (2008) ], a method to obtain features of a wave that are unaffected by dispersion, per mode, was developed for improving classification of underwater sounds (e.g., sonar backscatter). The current paper builds on this work and presents additional contributions. First, it is shown that the dispersion-invariant moments developed previously are not invariant to frequency-dependent attenuation (absorption); consequently, their classification performance degrades in such channels. Second, a feature extraction method is developed to obtain features that are invariant to dispersion, and to two forms of absorption (known a priori): namely, absorption that yields spectral magnitude attenuation (in dB) that is linear with frequency, and linear with log-frequency. Third, the relationship of these absorption- and dispersion-invariant moment (ADIM) features to the cepstrum of the wave is examined, and it is shown that cepstral moments are also invariant to dispersion, and to the first form of absorption for odd-order moments. Finally, simulations are conducted to illustrate the performance of the ADIMs and cepstral moments on classifying the backscatter from steel shells in a dispersive channel with absorption. Receiver operator characteristic curves quantify the superior discriminability of the ADIMs and cepstral moments compared to ordinary moments.

Acoustics Air-Sea Interaction & Remote Sensing Center for Environmental & Information Systems Center for Industrial & Medical Ultrasound Electronic & Photonic Systems Ocean Engineering Ocean Physics Polar Science Center
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