3,197 research outputs found
Bayesian Nonparametric Inference of Switching Linear Dynamical Systems
Many complex dynamical phenomena can be effectively modeled by a system that
switches among a set of conditionally linear dynamical modes. We consider two
such models: the switching linear dynamical system (SLDS) and the switching
vector autoregressive (VAR) process. Our Bayesian nonparametric approach
utilizes a hierarchical Dirichlet process prior to learn an unknown number of
persistent, smooth dynamical modes. We additionally employ automatic relevance
determination to infer a sparse set of dynamic dependencies allowing us to
learn SLDS with varying state dimension or switching VAR processes with varying
autoregressive order. We develop a sampling algorithm that combines a truncated
approximation to the Dirichlet process with efficient joint sampling of the
mode and state sequences. The utility and flexibility of our model are
demonstrated on synthetic data, sequences of dancing honey bees, the IBOVESPA
stock index, and a maneuvering target tracking application.Comment: 50 pages, 7 figure
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Online Discrimination of Nonlinear Dynamics with Switching Differential Equations
How to recognise whether an observed person walks or runs? We consider a
dynamic environment where observations (e.g. the posture of a person) are
caused by different dynamic processes (walking or running) which are active one
at a time and which may transition from one to another at any time. For this
setup, switching dynamic models have been suggested previously, mostly, for
linear and nonlinear dynamics in discrete time. Motivated by basic principles
of computations in the brain (dynamic, internal models) we suggest a model for
switching nonlinear differential equations. The switching process in the model
is implemented by a Hopfield network and we use parametric dynamic movement
primitives to represent arbitrary rhythmic motions. The model generates
observed dynamics by linearly interpolating the primitives weighted by the
switching variables and it is constructed such that standard filtering
algorithms can be applied. In two experiments with synthetic planar motion and
a human motion capture data set we show that inference with the unscented
Kalman filter can successfully discriminate several dynamic processes online
Gravity optimised particle filter for hand tracking
This paper presents a gravity optimised particle filter (GOPF) where the magnitude of the gravitational force for every particle is proportional to its weight. GOPF attracts nearby particles and replicates new particles as if moving the particles towards the peak of the likelihood distribution, improving the sampling efficiency. GOPF is incorporated into a technique for hand features tracking. A fast approach to hand features detection and labelling using convexity defects is also presented. Experimental results show that GOPF outperforms the standard particle filter and its variants, as well as state-of-the-art CamShift guided particle filter using a significantly reduced number of particles
Object Tracking and Mensuration in Surveillance Videos
This thesis focuses on tracking and mensuration in surveillance videos. The
first part of the thesis discusses several object tracking approaches based on the
different properties of tracking targets. For airborne videos, where the targets are
usually small and with low resolutions, an approach of building motion models for
foreground/background proposed in which the foreground target is simplified as a
rigid object. For relatively high resolution targets, the non-rigid models are applied.
An active contour-based algorithm has been introduced. The algorithm is based on
decomposing the tracking into three parts: estimate the affine transform parameters
between successive frames using particle filters; detect the contour deformation using
a probabilistic deformation map, and regulate the deformation by projecting the
updated model onto a trained shape subspace. The active appearance Markov chain
(AAMC). It integrates a statistical model of shape, appearance and motion. In the
AAMC model, a Markov chain represents the switching of motion phases (poses),
and several pairwise active appearance model (P-AAM) components characterize the
shape, appearance and motion information for different motion phases. The second
part of the thesis covers video mensuration, in which we have proposed a heightmeasuring
algorithm with less human supervision, more flexibility and improved
robustness. From videos acquired by an uncalibrated stationary camera, we first
recover the vanishing line and the vertical point of the scene. We then apply a single
view mensuration algorithm to each of the frames to obtain height measurements.
Finally, using the LMedS as the cost function and the Robbins-Monro stochastic
approximation (RMSA) technique to obtain the optimal estimate
Video foreground extraction for mobile camera platforms
Foreground object detection is a fundamental task in computer vision with many applications in areas such as object tracking, event identification, and behavior analysis. Most conventional foreground object detection methods work only in a stable illumination environments using fixed cameras. In real-world applications, however, it is often the case that the algorithm needs to operate under the following challenging conditions: drastic lighting changes, object shape complexity, moving cameras, low frame capture rates, and low resolution images. This thesis presents four novel approaches for foreground object detection on real-world datasets using cameras deployed on moving vehicles.The first problem addresses passenger detection and tracking tasks for public transport buses investigating the problem of changing illumination conditions and low frame capture rates. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modelling method with a human shape model into a weighted Bayesian framework to detect passengers. To deal with the problem of tracking multiple targets, we employ the Reversible Jump Monte Carlo Markov Chain tracking algorithm. Using the SVM classifier, the appearance transformation models capture changes in the appearance of the foreground objects across two consecutives frames under low frame rate conditions. In the second problem, we present a system for pedestrian detection involving scenes captured by a mobile bus surveillance system. It integrates scene localization, foreground-background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data.In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarity, and the second stage further clusters these aligned frames according to consistency in illumination. This produces clusters of images that are differential in viewpoint and lighting. A kernel density estimation (KDE) technique for colour and gradient is then used to construct background models for each image cluster, which is further used to detect candidate foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be detected.In addition to the second problem, we present three direct pedestrian detection methods that extend the HOG (Histogram of Oriented Gradient) techniques (Dalal and Triggs, 2005) and provide a comparative evaluation of these approaches. The three approaches include: a) a new histogram feature, that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated Gaussian filters (Leung and Malik, 2001) corresponding to the quantised orientation, which we refer to as the Histogram of Oriented Gradient Banks (HOGB) approach; b) the codebook based HOG feature with branch-and-bound (efficient subwindow search) algorithm (Lampert et al., 2008) and; c) the codebook based HOGB approach.In the third problem, a unified framework that combines 3D and 2D background modelling is proposed to detect scene changes using a camera mounted on a moving vehicle. The 3D scene is first reconstructed from a set of videos taken at different times. The 3D background modelling identifies inconsistent scene structures as foreground objects. For the 2D approach, foreground objects are detected using the spatio-temporal MRF algorithm. Finally, the 3D and 2D results are combined using morphological operations.The significance of these research is that it provides basic frameworks for automatic large-scale mobile surveillance applications and facilitates many higher-level applications such as object tracking and behaviour analysis
Dynamic Switching State Systems for Visual Tracking
This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together
Dynamic Switching State Systems for Visual Tracking
This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together
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