7 research outputs found
Towards an Interactive Humanoid Companion with Visual Tracking Modalities
The idea of robots acting as human companions is not a particularly new or original one. Since the notion of “robot ” was created, the idea of robots replacing humans in dangerous, dirty and dull activities has been inseparably tied with the fantasy of human-like robots being friends and existing side by side with humans. In 1989, Engelberger (Engelberger
Probabilistic three-dimensional object tracking based on adaptive depth segmentation
Object tracking is one of the fundamental topics of computer vision with diverse applications. The arising challenges in tracking, i.e., cluttered scenes, occlusion, complex motion, and illumination variations have motivated utilization of depth information from 3D sensors. However, current 3D trackers are not applicable to unconstrained environments without a priori knowledge. As an important object detection module in tracking, segmentation subdivides an image into its constituent regions. Nevertheless, the existing range segmentation methods in literature are difficult to implement in real-time due to their slow performance. In this thesis, a 3D object tracking method based on adaptive depth segmentation and particle filtering is presented. In this approach, the segmentation method as the bottom-up process is combined with the particle filter as the top-down process to achieve efficient tracking results under challenging circumstances. The experimental results demonstrate the efficiency, as well as robustness of the tracking algorithm utilizing real-world range information
Soft computing and non-parametric techniques for effective video surveillance systems
Esta tesis propone varios objetivos interconectados para el diseño de un sistema de vĂdeovigilancia cuyo funcionamiento es pensado para un amplio rango de condiciones. Primeramente se propone una mĂ©trica de evaluaciĂłn del detector y sistema de seguimiento basada en una mĂnima referencia. Dicha tĂ©cnica es una respuesta a la demanda de ajuste de forma rápida y fácil del sistema adecuándose a distintos entornos. TambiĂ©n se propone una tĂ©cnica de optimizaciĂłn basada en Estrategias Evolutivas y la combinaciĂłn de funciones de idoneidad en varios pasos. El objetivo es obtener los parámetros de ajuste del detector y el sistema de seguimiento adecuados para el mejor funcionamiento en una amplia gama de situaciones posibles Finalmente, se propone la construcciĂłn de un clasificador basado en tĂ©cnicas no paramĂ©tricas que pudieran modelar la distribuciĂłn de datos de entrada independientemente de la fuente de generaciĂłn de dichos datos. Se escogen actividades detectables a corto plazo que siguen un patrĂłn de tiempo que puede ser fácilmente modelado mediante HMMs. La propuesta consiste en una modificaciĂłn del algoritmo de Baum-Welch con el fin de modelar las probabilidades de emisiĂłn del HMM mediante una tĂ©cnica no paramĂ©trica basada en estimaciĂłn de densidad con kernels (KDE). _____________________________________This thesis proposes several interconnected objectives for the design of a video-monitoring
system whose operation is thought for a wide rank of conditions.
Firstly an evaluation technique of the detector and tracking system is proposed and it is based
on a minimum reference or ground-truth. This technique is an answer to the demand of fast and
easy adjustment of the system adapting itself to different contexts.
Also, this thesis proposes a technique of optimization based on Evolutionary Strategies and
the combination of fitness functions. The objective is to obtain the parameters of adjustment of
the detector and tracking system for the best operation in an ample range of possible situations.
Finally, it is proposed the generation of a classifier in which a non-parametric statistic technique
models the distribution of data regardless the source generation of such data. Short term
detectable activities are chosen that follow a time pattern that can easily be modeled by Hidden
Markov Models (HMMs). The proposal consists in a modification of the Baum-Welch algorithm
with the purpose of modeling the emission probabilities of the HMM by means of a nonparametric
technique based on the density estimation with kernels (KDE)
Adaptive Kernel Density Approximation and Its Applications to Real-Time Computer Vision
Density-based modeling of visual features is very common in computer vision research due to the uncertainty of observed data; so accurate and
simple density representation is essential to improve the quality of overall systems.
Even though various methods, either parametric or non-parametric, are proposed for density modeling, there is a significant trade-off between flexibility and computational complexity.
Therefore, a new compact and flexible density representation is necessary, and the dissertation provides a solution to alleviate the problems as
follows.
First, we describe a compact and flexible representation of probability density functions using a mixture of Gaussians which is called Kernel
Density Approximation (KDA). In this framework, the number of Gaussians components as well as the weight,
mean, and covariance of each Gaussian component are determined automatically by mean-shift mode-finding procedure and curvature fitting. An original density function estimated by kernel density estimation is
simplified into a compact mixture of Gaussians by the proposed method; memory requirements are dramatically reduced while incurring only a small amount of error.
In order to adapt to variations of visual features, sequential kernel density approximation is proposed in which a sequential update of the density function is performed in linear time.
Second, kernel density approximation is incorporated into a Bayesian filtering framework, and we design a Kernel-based Bayesian Filter (KBF). Particle filters have inherent limitations such as degeneracy or
loss of diversity which are mainly caused by sampling from discrete proposal distribution. In kernel-based Bayesian filtering, every relevant probability density function is continuous and the posterior is simplified by kernel density approximation so as to propagate a compact form of the density function
from step to step. Since the proposal distribution is continuous in this framework, the problems in conventional particle filters are alleviated.
The sequential kernel density approximation technique is naturally applied to background modeling, and target appearance modeling for object tracking.
Also, the kernel-based Bayesian filtering framework is applied to object tracking, which shows improved performance with a smaller number of samples.
We demonstrate the performance of kernel density approximation and its application through various simulations and experiments with real videos
Trust-Region Methods for Real-Time Tracking
Optimization methods based on iterative schemes can be divided into two classes: linesearch methods and trustregion methods. While linesearch techniques are commonly found in various vision applications, not much attention is paid to trust-region methods. Motivated by the fact that linesearch methods can be considered as special cases of trust-region methods, we propose to apply trust-region methods to visual tracking problems. Our approach integrates trust-region methods with the Kullback Leibler distance to track a rigid or non-rigid object in real-time. If not limited by the speed of a camera, the algorithm can achieve frame rate above 60 fps. To justify our method, a variety of experiments/comparisons are carried out for the trust-region tracker and a linesearch-based mean-shift tracker with same initial conditions. The experimental results support our conjecture that a trust-region tracker should perform superiorly to a linesearch one. 1