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.

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.

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.

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.

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.

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.

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.