Performance bounds of extended target tracking in cluttered environments

In this paper, we consider the problem of the performance bound of a nonlinear filtering problem corresponding to tracking an extended target in cluttered environments (i.e., with false alarms and missed detections). The high resolution sensor obtains the measurements of the position and the extent of the extended target whose shape is modeled by an ellipse. The posterior Cramer-Rao lower bound (PCRLB) provides a useful tool to evaluate the best achievable performance of the nonlinear filtering problem. The bounds of the traditional kinematic state estimation are calculated using the point and the extended target model. It is shown in this paper that the bound of extended target tracking is smaller than that of point model because more information is utilized. The bounds are calculated to examine the influence of the measuring accuracy, the geometry between the sensor and the target, the prior knowledge of the target, and the environmental circumstance. In a cluttered environment, the PCRLB is calculated by IRF (information reduction factor), MSC (measurement sequence conditioning), and MESC (measurement existence sequence conditioning) approaches. The simulation results also illustrate the relationship of the three methods.     

Consistency and robustness of PDAF for target tracking in cluttered environments

This paper presents a simulation-based investigation of the consistency of the PDAF via statistical tests of its actual errors versus the filter calculated covariances. These tests confirm the goodness of the approximation done at every stage in the PDAF, where a Gaussian mixture is replaced by an ‘umbrella’ Gaussian using moment matching. Two versions of the PDAF are examined: a parametric one that uses a Poisson model for the number of clutter originated measurements and a nonparametric one that uses a diffuse prior. While no new theoretical results are presented, the methodology of testing filter consistency can prove to be useful in the general evaluation of filters that contain approximations.     

Smooth quadrotor trajectory generation for tracking a moving target in cluttered environments

In this paper,we present a trajectory generation method of a quadrotor,based on the optimal smoothing B-spline,for tracking a moving target with consideration o...     

Inference-based surface reconstruction of cluttered environments

We present an inference-based surface reconstruction algorithm that is capable of identifying objects of interest among a cluttered scene, and reconstructing solid model representations even in the presence of occluded surfaces. Our proposed approach incorporates a predictive modeling framework that uses a set of user-provided models for prior knowledge, and applies this knowledge to the iterative identification and construction process. Our approach uses a local to global construction process guided by rules for fitting high-quality surface patches obtained from these prior models. We demonstrate the application of this algorithm on several example data sets containing heavy clutter and occlusion.     

Manoeuvring multitarget tracking method in cluttered environment

A method for tracking a manoeuvring multitarget in a cluttered environment is presented. The clutter or false alarms are assumed to occur uniformly and to be independently distributed. The algorithm is performed by taking a sliding window of length uT (T is the sampling time) at time K. Instead of solving a large problem, the entire set of targets and measurements is divided into clusters so that a number of smaller problems are solved independently. When a set of measurements is received, we form a new data-association hypothesis for the set of measurements lying in the validation gales; with each cluster from time K — u + 1 to K the probability of each track history is computed, and ihen by choosing the largest of these histories we perform the target measurement updated with the adaptive state esiimator. Meanwhile, the covariance-matching technique is adopted so that the accuracy of the adaptive state estimator will be improved. Simulation has shown the effectiveness of the tracking algorithm.     

Object segmentation in cluttered and visually complex environments

Object segmentation is essential for systems that acquire object models online for robotic grasping. However, it remains a major technical challenge in visually complex and uncontrolled environments. Segmentation algorithms that rely on image features alone can perform poorly under certain lighting conditions, or if the object and the background have similar appearance. In parallel, known object segmentation algorithms that rely exclusively on three dimensional (3D) geometric data are derived under strong assumptions about the geometry of the scene. A promising approach to performing object segmentation is to use a combination of appearance and 3D features. In this paper, an object segmentation algorithm is presented that combines multiple appearance and geometric cues. The segmentation is formulated as a binary labeling problem. The Conditional Random Fields (CRF) framework is used to model the conditional probability of the labeling given the appearance and geometric data. The maximum a posteriori estimation of the labeling is obtained by minimizing the energy function corresponding to the CRF using graph cuts. A simple and efficient method for initializing the proposed algorithm is also presented. Experimental results have demonstrated the effectiveness of the proposed algorithm.     

Distributed particle filter based speaker tracking in distributed microphone networks under non-Gaussian noise environments

Non-Gaussian noise distorts the speech signals and leads to the degradation of speaker tracking performance. In this paper, a distributed particle filter (DPF) based speaker tracking method in distributed microphone networks under non-Gaussian noise and reverberant environments is proposed. A generalized correntropy function is first presented to estimate the time differences of arrival (TDOA) for speech signals at each node in distributed microphone networks. Next, to address spurious TDOA estimations caused by noise and reverberation, a multiple-hypothesis likelihood model is introduced to calculate the local likelihood functions of the DPF. Finally, a DPF fusing local likelihood functions with an average consensus algorithm is employed to estimate a moving speaker's positions. The proposed method can accurately track the speaker under non-Gaussian noise and reverberant environments, and it is scalable and robust against the nodes failure in distributed networks. Simulation experiments show the validation of the proposed speaker tracking method.     

Audio-visual speaker diarization using fisher linear semi-discriminant analysis

Speaker diarization aims to automatically answer the question “who spoke when” given a speech signal. In this work, we have focused on applying the FLsD approach, a semi-supervised version of Fisher Linear Discriminant analysis, both in the audio and the video signals to form a complete multimodal speaker diarization system. Extensive experiments have proven that the FLsD method boosts the performance of the face diarization task (i.e. the task of discovering faces over time given only the visual signal). In addition, we have proven through experimentation that applying the FLsD method for discriminating between faces is also independent of the initial feature space and remains relatively unaffected as the number of faces increases. Finally, a fusion method is proposed that leads to performance improvement in comparison to the best individual modality, which is the audio signal.     

Robust shape tracking in the presence of cluttered background

Many object-tracking algorithms are based on low-level features detected in the image. Typically, the object shape and position are estimated to fit the observed features. Unfortunately, image analysis methods often produce invalid features (outliers) which do not belong to the object boundary. These features have a strong influence on the shape estimates, leading to meaningless tracking results. This paper proposes a robust tracking algorithm which is able to deal with outliers, inspired in the probabilistic data association filter proposed in the context of point tracking. The algorithm is based on two key concepts. First, middle level features (strokes) are used instead of low-level ones (edge points). Second, two labels (valid/invalid) are considered for each stroke. Since the stroke labels are unknown all labeling sequences are considered and a probability (confidence degree) is assigned to each of them. In this way, all the strokes contribute to track the moving object but with different weights. This allows a robust performance of the tracker in the presence of outliers. Experimental tests are provided to assess the performance of the proposed algorithm in lip and gesture tracking and surveillance applications.     

Visibility-based spatial reasoning for object manipulation in cluttered environments

In this paper, we present visibility-based spatial reasoning techniques for real-time object manipulation in cluttered environments. When a robot is requested to manipulate an object, a collision-free path should be determined to access, grasp, and move the target object. This often requires processing of time-consuming motion planning routines, making real-time object manipulation difficult or infeasible, especially in a robot with a high DOF and/or in a highly cluttered environment. This paper places special emphasis on developing real-time motion planning, in particular, for accessing and removing an object in a cluttered workspace, as a local planner that can be integrated with a general motion planner for improved overall efficiency. In the proposed approach, the access direction of the object to grasp is determined through visibility query, and the removal direction to retrieve the object grasped by the gripper is computed using an environment map. The experimental results demonstrate that the proposed approach, when implemented by graphics hardware, is fast and robust enough to manipulate 3D objects in real-time applications.     

Collision-free path planning of cable-driven parallel robots in cluttered environments

Intelligent Service Robotics - Path planning of cable-driven parallel robots (CDPRs) is a challenging task due to cables which may cause various collisions. In this paper, three steps are suggested...     

Life detection and non-contact respiratory rate measurement in cluttered environments

Multimedia Tools and Applications - A method is proposed in this paper for life detection and non-contact respiratory rate measurement in cluttered environments. Only an RGB video of the detection...     

Spreading algorithm for efficient vegetation detection in cluttered outdoor environments

This paper investigates the problem of detecting vegetation in unstructured environments for guiding an autonomous robot safely, exploiting its mobility capability in a cluttered outdoor environment. The aim is to create an adaptive learning algorithm which performs a quantitatively accurate detection that is fast enough for a real-time application. Chlorophyll-rich vegetation pixels are selected by thresholding vegetation indices, and then are considered as the seeds of a “spread vegetation”. For each seed pixel, a convex combination of color and texture dissimilarities is used to infer the difference between the pixel and its neighbors. The convex combination, trained via semi-supervised learning, models either the difference of vegetation pixels or the difference between a vegetation pixel and a non-vegetation pixel, and thus allows a greedy decision-making process to expand the spread vegetation, so-called vision-based spreading. To avoid overspreading, especially in the case of noise, a spreading scale is set. On the other hand, another vegetation spreading based on spectral reflectance is carried out in parallel. Finally, the intersection part resulting from both the vision-based and spectral reflectance-based methods is added to the spread vegetation. The approach takes into account both vision and chlorophyll light absorption properties. This enables the algorithm to capture much more detailed vegetation features than does prior art, and also give a much richer experience in the interpretation of vegetation representation, even for scenes with significant overexposure or underexposure as well as with the presence of shadow and sunshine. In all real-world experiments we carried out, our approach yields a detection accuracy of over 90%.     

在噪声环境下的分级说话人辨识

将小波变换与维纳滤波结合起来对语音进行去噪.为了提高系统的鲁棒性和辨识率,在采用分级说话人辨识的基础上,将基音周期的高斯概率密度对GMM分类器的似然度进行加权,形成新的似然度进行说话人辨识.实验结果显示,所提出系统的鲁棒性和辨识率都有所提高.     

Audio-visual speech recognition techniques in augmented reality environments

Many recent studies show that Augmented Reality (AR) and Automatic Speech Recognition (ASR) technologies can be used to help people with disabilities. Many of these studies have been performed only in their specialized field. Audio-Visual Speech Recognition (AVSR) is one of the advances in ASR technology that combines audio, video, and facial expressions to capture a narrator’s voice. In this paper, we combine AR and AVSR technologies to make a new system to help deaf and hard-of-hearing people. Our proposed system can take a narrator’s speech instantly and convert it into a readable text and show the text directly on an AR display. Therefore, in this system, deaf people can read the narrator’s speech easily. In addition, people do not need to learn sign-language to communicate with deaf people. The evaluation results show that this system has lower word error rate compared to ASR and VSR in different noisy conditions. Furthermore, the results of using AVSR techniques show that the recognition accuracy of the system has been improved in noisy places. Also, the results of a survey that was conducted with 100 deaf people show that more than 80 % of deaf people are very interested in using our system as an assistant in portable devices to communicate with people.     

Avoiding obstacles - multisensor navigation for nonholonomic robots in cluttered environments

This article introduces a navigation method for nonholonomic (differential drive) vehicles, based on odometry, regularly reset by a vision-based self-localization algorithm, and endowed with a sonar-based obstacle avoidance and guidance control algorithm that does not rely on path planning. The guidance controller is used in the soccer robots of the RoboCup middle-size league (MSL) ISocRob team, fully integrated in the state machine that coordinates task execution. The algorithm can be generally applied to structured indoor environments, provided that visual features can be observed by the self-localization method and that the visual information is not ambiguous.     

The combinatorics of heuristic search termination for objectrecognition in cluttered environments

Many current recognition systems terminate a search once an interpretation that is good enough is found. The author formally examines the combinatorics of this approach, showing that choosing correct termination procedures can dramatically reduce the search. In particular, the author provides conditions on the object model and the scene clutter such that the expected search is at most quartic. The analytic results are shown to be in agreement with empirical data for cluttered object recognition. These results imply that it is critical to use techniques that select subsets of the data likely to have come from a single object before establishing a correspondence between data and model features     

Dimensional synthesis of kinematically redundant serial manipulators for cluttered environments

A systematic procedure for synthesizing kinematically redundant serial manipulators is proposed in this paper. For a given cluttered workcell, the task space locations (TSL’s) for the desired manipulator are prescribed. The synthesis is performed with the objective of reachability of the manipulator at specified TSL’s, while avoiding obstacles. The problem is formulated as a constrained optimization problem, minimizing the positional error and simultaneously avoiding any collision of the manipulator with either the obstacles or within its links. The technique used to solve the resulting constrained optimization problem is the classical Augmented Lagrangian Method. The paper presents a discussion on the past works in this field. It is observed that the presented literature is confined to special cases only while the proposed method involves full generality of the synthesis problem. The availability of such an algorithm working for full generality is important, particularly for highly constrained environments. The efficiency of the proposed approach to synthesize the desired redundant manipulators is exhibited through diverse cases. The resulting synthesized manipulators are further checked for the possibility of feasible paths between TSL’s. An outline of the development of a redundant manipulator, synthesized through the presented approach, is also included in this paper.