Showing papers presented at "International Conference on Control, Automation, Robotics and Vision in 2010"

tinySLAM: A SLAM algorithm in less than 200 lines C-language program

Abstract: This paper presents a Laser-SLAM algorithm which can be programmed in less than 200 lines C-language program. The first idea aimed to develop and implement a simple SLAM algorithm providing good performances without exceeding 200 lines in a C-language program. We use a robotic platform called MinesRover, a six wheels robot with several sensors. We based our work and calculations on a laser sensor and the odometry of the robot. The article presents the different capabilities of the platform and the way we use them in order to improve our programs. We also illustrates the difficulties encountered during the programming and testing of the algorithm. This work shows the possibility to perform complex tasks using simple and easily programmable algorithms.

Convergence and Mean Square Stability of Suboptimal Estimator for Systems With Measurement Packet Dropping

Abstract: This paper is concerned with estimation problem for discrete-time systems with packet dropping. A new optimal filter is derived by minimizing the mean squared estimation error. An optimal smoother is also derived in a similar way. Both estimators are designed by solving one deterministic Riccati equation. Both the convergence of the estimation error covariance and mean square stability of the estimator are proved under standard assumption. It is shown that the new estimator has smaller error covariance and has wider applications as compared with the MMSE estimator. One of the key techniques adopted in this paper is the introduction of the innovation sequence for the multiplicative noise systems.

Generating UAV communication networks for monitoring and surveillance

Abstract: An important use of unmanned aerial vehicles is surveillance of distant targets, where sensor information must quickly be transmitted back to a base station. In many cases, high uninterrupted bandwidth requires line-of-sight between sender and transmitter to minimize quality degradation. Communication range is typically limited, especially when smaller UAVs are used. Both problems can be solved by creating relay chains for surveillance of a single target, and relay trees for simultaneous surveillance of multiple targets. In this paper, we show how such chains and trees can be calculated. For relay chains we create a set of chains offering different trade-offs between the number of UAVs in the chain and the chain's cost. We also show new results on how relay trees can be quickly calculated and then incrementally improved if necessary. Encouraging empirical results for improvement of relay trees are presented.

Improved genetic algorithms based optimum path planning for mobile robot

Abstract: Improved genetic algorithms incorporate other techniques, methods or algorithms to optimize the performance of genetic algorithm. In this paper, improved genetic algorithms of optimum path planning for mobile robot navigation are proposed. An Obstacle Avoidance Algorithm (OAA) and a Distinguish Algorithm (DA) are introduced to generate the initial population in order to improve the path planning efficiency to select only the feasible paths during the evolution of genetic algorithm. Domain heuristic knowledge based crossover, mutation, refinement and deletion operators are specifically designed to fit path planning for mobile robots. Proposed genetic algorithms feature unique, simple path representations, and simple but effective evaluation methods. Simulation studies and real time implementations are carried out to verify and validate the effectiveness of the proposed algorithms.

MCC: A baseline algorithm for fingerprint verification in FVC-onGoing

Abstract: This paper describes an improved version of the MCC fingerprint matching approach. An in-depth error analysis allowed us to point out the weakest points of the original MCC and to design: i) a more effective minutiae pair selection and ii) a more distortion-tolerant relaxation. The parameters of the new version have been tuned over a new larger dataset and the final algorithm has been evaluated on FVC-onGoing. The results show that MCC compares favorably with some of the most accurate commercial algorithms published in FVC-onGoing.

Dense SIFT and Gabor descriptors-based face representation with applications to gender recognition

Abstract: In this paper, a novel face representation in terms of dense local image descriptors is proposed. Scale Invariant Feature Transform (SIFT) and Gabor, two of the most popular local image descriptors, at dense grid pixels of a face image are used to represent the face. The efficiency of the representation has been investigated in gender recognition. There are four problems when applying the SIFT to facial gender recognition. (1) There may be only a few keypoints that can be found in a face image due to the missing texture and ill-illumined faces; (2) The SIFT descriptors at the keypoints (we called it sparse SIFT) are distinctive whereas alternative descriptors at non-keypoints (e.g. grid) could cause negative impact on the accuracy; (3) Most of the existing methods employ SIFT descriptors matching in which relative larger image size is required in order that enough keypoints can be found to support the matching and (4) The matching is assumed that the faces are well registered. We provide solutions to the above difficulties in this paper and the problem of recognizing gender using the combination of SIFT descriptors and Gabor of face images is studied. The Gabor representations of the face images are fused with the dense SIFT at the feature-level to improve the accuracy. AdaBoost is adopted to select features and form a strong classifier. The experimental results on a large set of faces have shown that the proposed method can achieve high accuracies even for faces that are not aligned.

Fruit detection, tracking, and 3D reconstruction for crop mapping and yield estimation

Abstract: Through automated agricultural inspection, farmers can potentially achieve better productivity and accurately predict yields and crop quality. A variety of sensors can be used for agricultural inspection, but the cheapest and most information-rich is the video camera. We collect data in the field from a monocular camera fixed to a mobile inspection platform. For purposes of pineapple crop mapping and yield prediction, we propose an image processing framework for in-field fruit detection, tracking, and 3D reconstruction. We perform a series of experiments on feature point extraction using Harris, SIFT, and SURF features, feature point description using SIFT and SURF descriptors, feature point classification using SVMs, fruit region tracking using blob tracking, and 3D reconstruction using structure from motion and robust ellipsoid estimation techniques. We find that SURF feature points and descriptors provide the best tradeoff between processing time and classification accuracy and that the method is sufficiently accurate for fruit region detection. Our preliminary results for fruit region tracking and 3D fruit reconstruction are promising. We plan further work towards development of a useful aid to help farmers manage their farms.

Subspace identification and model predictive control for buildings

Abstract: Model predictive controller presented in this article makes use of both weather forecast and thermal model of a building to control inside temperature. This, by sharp contrast to conventional control strategies such as weather-compensated heating control (heating-curve controller), enables utilization of thermal capacity of the building, thus minimization of energy consumption. The inside temperature can be maintained at desired levels independent of the outside weather conditions using modified formulation of predictive controller. Nevertheless, proper identification of the building model is crucial. The models of multiple-input multiple-output systems can be identified using subspace methods. The controller was tested on (and applied to) the real building and results were compared with a present heating control.

A benchmarking tool for MAV visual pose estimation

Abstract: The large collections of datasets for researchers working on the Simultaneous Localization and Mapping problem are mostly collected from sensors such as wheel encoders and laser range finders mounted on ground robots. The recent growing interest in doing visual pose estimation with cameras mounted on micro-aerial vehicles however has made these datasets less useful. In this paper, we describe our work in creating new datasets collected from a sensor suite mounted on a quadrotor platform. Our sensor suite includes a forward looking camera, a downward looking camera, an inertial measurement unit and a Vicon system for groundtruth. We propose the use our datasets as benchmarking tools for future works on visual pose estimation for micro-aerial vehicles. We also show examples of how the datasets could be used for benchmarking visual pose estimation algorithms.

Eye-gaze detection with a single WebCAM based on geometry features extraction

Abstract: In this paper, we propose an efficient approach for real-time eye-gaze detection from images acquired from a web camera. The measured data is sufficient to describe the eye movement, because the web camera is stationary with respect to the head. First, the image is binarized with a dynamic threshold. Then geometry features of the eye image are extracted from binary image. Next using estimation method based on geometry structure of eye, we detect the positions of two eye corners. After that, the center of iris is detected by matching between an iris boundary model and image contours. Finally, using the relative position information between the center of iris and the eye corners, base on the relationship between image coordinate and monitor coordinate, the position where the eye is looking at the monitor is calculated. This system requires only a low cost web camera and a personal computer. Experimental results show that the proposed system can detect accurately eye movements in realtime.

Reaching law based sliding mode control for discrete MIMO systems

Abstract: This paper analyzes the effectiveness of the reaching law approach for sliding mode control of discrete time systems. The method is extended for sliding mode control for multi-input multi-output (MIMO) systems. Modified reaching law for reducing the chattering is proposed. The performance of the proposed controller is analyzed by carrying out simulation studies on MIMO systems. The performance of the controller is also compared with that of the equivalent control.

Detection of unfocused raindrops on a windscreen using low level image processing

Abstract: In a scene, rain produces a complex set of visual effects. Obviously, such effects may infer failures in outdoor vision-based systems which could have important side-effects in terms of security applications. For the sake of these applications, rain detection would be useful to adjust their reliability. In this paper, we introduce the problem (almost unprecedented) of unfocused raindrops. Then, we present a first approach to detect these unfocused raindrops on a transparent screen using a spatio-temporal approach to achieve detection in real-time. We successfully tested our algorithm for Intelligent Transport System (ITS) using an on-board camera and thus, detected the raindrops on the windscreen. Our algorithm differs from the others in that we do not need the focus to be set on the windscreen. Therefore, it means that our algorithm may run on the same camera sensor as the other vision-based algorithms.

Face occlusion detection by using B-spline active contour and skin color information

Abstract: This paper proposes a face occlusion verification method for an automated teller machine (ATM) application. The proposed method mainly consists of three steps. Firstly, a head and shoulder shape is detected by applying B-spline active contour to motion edges. This motion edge is generated by a kurtosis-based frame selection and distance transformation-based motion edge detection. Secondly, a face area is estimated by fitting an ellipse to the detected head and shoulder shape. Finally, occlusion of the face area is determined by measuring skin color area ratio (SCAR) of whole face area and facial component areas. Experimental results show that the proposed head and shoulder detection method has 94.8% detection rate even though there are various types of severe occlusions in faces, and the proposed occlusion verifier has 86.7% verification rate.

Obstacle-free range determination for rail track maintenance vehicles

Abstract: Maintenance trains travel in convoy. In Australia, only the first train of the convoy pays attention to the track sig-nalization (the other convoy vehicles simply follow the preceding vehicle). Because of human errors, collisions can happen between the maintenance vehicles. Although an anti-collision system based on a laser distance meter is already in operation, the existing system has a limited range due to the curvature of the tracks. In this paper, we introduce an anti-collision system based on vision. The two main ideas are, (1) to warp the camera image into an image where the rails are parallel through a projective transform, and (2) to track the two rail curves simultaneously by evaluating small parallel segments. The performance of the system is demonstrated on an image dataset.

Retinal blood vessel segmentation via graph cut

Abstract: Image analysis is becoming increasingly prominent as a non intrusive diagnosis in modern ophthalmology. Blood vessel morphology is an important indicator for diseases like diabetes, hypertension and retinopathy. This paper presents an automated and unsupervised method for retinal blood vessels segmentation using the graph cut technique. The graph is constructed using a rough segmentation from a pre-processed image together with spatial pixel connection. The proposed method was tested on two public datasets and compared with other methods. Experimental results show that this method outperforms other unsupervised methods and demonstrate the competitiveness with supervised methods.

Model predictive control based efficient operation of battery energy storage system for primary frequency control

Abstract: This paper presents a method for an efficient operation of a battery energy storage system (BESS) associated with frequency control problem. A control system model is proposed to simulate the BESS for frequency control application. A controller based on model predictive control (MPC) is designed for the reliable operation of the BESS for primary frequency regulation. A frequency prediction model based on Grey theory is also designed to optimize the performance of our predictive controller. The method is tested using real measurements from a real power grid in the presence of multiple and realistic physical system constraints. The simulation results depict the effectiveness of the proposed frequency regulation scheme.

A comparative study of local feature extraction for age estimation

Abstract: Many age estimation methods have been proposed for various applications such as Age Specific Human Computer Interaction (ASHCI) system, age simulation system and so on. Because the performance of the age estimation is greatly affected by the aging feature, the aging feature extraction from facial images is very important. The aging features used in previous works can be divided into global and local features. As global features, Active Appearance Models (AAM) was mainly used for age estimation in previous works. However, AAM is not enough to represent local features such as wrinkle and skin. Therefore, the research about local features is required. In previous works, local features were generally used to determine age group rather than detailed age, and the comparative studies about various local features extraction methods were not conducted. In this paper, the performances of sobel filter, difference image between original and smoothed image, ideal high pass filter (IHPF), gaussian high pass filter (GHPF), Haar and Daubechies discrete wavelet transform (DWT) are compared for extracting local features and detailed age estimation is performed by Support Vector Regression (SVR) on BERC and PAL aging database. The experimental results show that local features can be used for detailed age estimation and GHPF gives a better performance than other methods.

A lidar perception scheme for intelligent vehicle navigation

Abstract: In urban environments, detection of moving obstacles and free space determination are key issues for driving assistance systems or autonomous vehicles. This paper presents a lidar-based perception system for passenger-cars, able to do simultaneously mapping and moving obstacles detection. Nowadays, many lidars provide multi-layer and multi-echo measurements. A smart way to handle this multi-modality is to use grids projected on the road surface in both global and local frames. The global one generates the mapping and the local is used to deal with moving objects. An approach based on both positive and negative accumulation has been developed to address the remnant problem of quickly moving obstacles. This method is also well suited for multi-layer and multi-echo sensors. Experimental results carried out with an IBEO Alasca and an Applanix positioning system show the performance of such a perception strategy.

Fractional-Order Generalized Predictive Control: Formulation and some properties

Abstract: This paper deals with the formal formulation of Fractional-Order Generalized Predictive Control, a generalization of GPC that makes use of fractional-order operators in its cost function. A thorough mathematical description of FGPC is given and the main similarities and differences between GPC and FGPC are highlighted.

Automated planning for collaborative UAV systems

Abstract: Mission planning for collaborative Unmanned Aircraft Systems (UAS:s) is a complex topic which involves trade-offs between the degree of centralization or decentralization required, the degree of abstraction in which plans are generated, and the degree to which such plans are distributed among participating UAS:s. In realistic environments such as those found in natural and man-made catastrophes where emergency services personnel are involved, a certain degree of centralization and abstraction is necessary in order for those in charge to understand and eventually sign off on potential plans. It is also quite often the case that unconstrained distribution of actions is inconsistent with the loosely coupled interactions and dependencies which arise between collaborating systems. In this article, we present a new planning algorithm for collaborative UAS:s based on combining ideas from forward chaining planning with partial-order planning leading to a new hybrid partial order forward-chaining (POFC) framework which meets the requirements on centralization, abstraction and distribution we find in realistic emergency services settings.

A real-time robust global localization for autonomous mobile robots in large environments

Abstract: Global localization aims to estimate a robot's pose in a learned map without any prior knowledge of its initial pose. Achieving highly accurate global localization remains a challenge for autonomous mobile robots especially in large-scale unstructured outdoor environments. This paper introduces a real-time reliable global localization approach with the capability of addressing the kidnapped robot problem using only laser sensors. Our approach includes four steps: 1) local Simultaneous Localization and Mapping 2) map matching 3) position tracking and 4) localization quality evaluation. For sensor perception, we use occupancy grid method to represent robot environment. A novel pyramid grid-map based coarse-to-fine matching approach is proposed to improve the localization accuracy. Experimental results including an outdoor environment of 25,000 m2 are presented to validate the feasibility and reliability of the proposed approach.

Backstepping based nested multi-loop control laws for a quadrotor

Abstract: Quadrotors are suitable testbeds for several point to point hover based surveillance and observation tasks. As such, the hover mode is a very important mode of operation for a quadrotor platform. This paper describes the design of a hover mode controller using a backstepping based multi-loop design for a linearized quadrotor dynamics. This controller is then tested using both the linear and a full nonlinear simulation model of the quadrotor.

License plate localisation based on morphological operations

Abstract: Automatic Number Plate Recognition (ANPR) systems allow users to track, identify and monitor moving vehicles by automatically extracting their number plates. This paper presents an improved method to locate car plates in an ANPR system. The proposed method is based on morphological open and close operations where different Structuring Elements (SE) are used to maximally eliminate non-plate region and enhance plate region. This method has been tested using a database of UK number plates and results achieved have shown significant improvements in terms of the detection rate compare to other existing plate localisation systems.

Vision-based lane departure detection system in urban traffic scenes

Abstract: A lane-departure detection system without intrinsic and extrinsic camera parameters calibration is proposed in this paper. The proposed system, which provides driving safety by lane detection and lane departure warning, is focused on urban road with complicated lane marks instead of simple highway scenes. Due to the complexity of urban traffic scenes, false lane detections are highly caused by warning lines and signs whose shape and color are similar to the lane boundary. In this study, two lane features, lane width and lane boundary continuity, are proposed to obtain reliable and quality lane detected results on urban traffic scenes. Lane departure warning decisions are then applied to provide driving safety. Experimental results of urban road sceneries have demonstrated feasibility and robustness of the proposed method.

Facial part displacement effect on template-based gender and ethnicity classification

Abstract: Visual information such as gender, age and ethnicity play critical roles in human identification. Most of gender and ethnicity recognition research works use the full face considering equal discriminant capability for different face parts. In this paper, we improve the gender and ethnicity recognition, by employing the optimum decision making rule on the confidence level of automatically separated face regions using the modified Golden ratio mask. Faces are preprocessed with multiple base point photometric normalization to prevent facial parts displacement in the noted mask, due to different facial parts' distances of people. SVM is employed as the classifier on the extracted Gabor features of each patch to get its confidence level. The final classification results are obtained based on the output of each patch decision using the optimum decision making rule. Finally, using the most accurate normalization approach for each patch, we could achieve 94% and 98% for gender and ethnicity respectively on a dataset composed of FERET and PEAL frontal face images.

Players tracking and ball detection for an automatic tennis video annotation

Abstract: This paper describes our algorithms for players tracking and ball detection for an automatic broadcast tennis video annotation. The system detects and tracks the players using a robust non-parametric procedure for estimating density gradients called the mean shift algorithm. The basic mean shift tracking algorithm assumes that the target object has to separate sufficiently from background, but this assumption is not always true especially when tracking is carried out in dynamic backgrounds such as in sport videos. To cope with this problem, in our proposed system, we embrace the motion segmentation and use the 8×8×8 color histogram to be feature distribution for mean shift tennis players tracking. In order to determine the players' actions precisely, the system also detect and track ball positions using frame differencing as well as applying some correlation techniques to eliminate false detections. Based on both players' motion patterns and ball positions, the system can precisely classify the players' action into backhand ground stroke and forehand ground stroke. Videos of broadcast tennis games downloaded from the Internet have been tested. The results show our system is able to precisely classify the player's actions with 83.7% precision and 82% recall rates.

Adaptive region tracking control for autonomous underwater vehicle

Abstract: This paper presents an adaptive region tracking control for Autonomous Underwater Vehicle (AUV). The AUV is required to track a moving region to accomplish a given task. The desired target is specified as a region rather than a point so that the control effort used to track the region is minimal. In the applications where the accuracy is of utmost importance, the desired region can be chosen to be small so that the precision is not lost. The desired region can be scaled up or scaled down so that the AUV can adjust its position to suit the applications. A Lyapunov-like function is presented for the stability analysis. Simulation results on AUV with 6 degrees of freedom are presented to demonstrate the effectiveness of the proposed controller.

Generating cancelable biometrie templates using a projection line

Abstract: Biometrie authentication systems can address the problems of genuine user verification and usability suffered by the conventional cryptosystems based on the password and token. However, as biometrics is not replaceable, its privacy has become a concern. Also, if the same biometrics is used in multiple applications, it will be vulnerable to potential cross-matching attacks. In this paper, a projection line method is proposed for cancelable fingerprint template design. The main advantage of this method is its simplicity. Because there exist many possibilities projecting minutia into the projection line controlled by the whole range of possible projection line properties, the proposed approach offers non-invertible transformation and also possess good revocability property.

Framework of WSN based human centric cyber physical in-pipe water monitoring system

Abstract: We present a human centric cyber physical framework architecture of our in-pipe water monitoring and feedback system. This system comprises of the physical water distribution infrastructure, together with the hardware and software supported intelligent agents for water allotment, leak detection and contamination spread control. An agent-based approach for connecting the cyber and physical layers is selected, where the agents get information from sensors monitoring the physical components and provide this information to the cyber system. The Hidden Markov Models (HMM) are briefly discussed to determine water consumption patterns and make decisions for cyber and physical systems.

Design and implementation of a contactless palm print and palm vein sensor

Abstract: This paper presents an innovative contactless palm print and palm vein recognition system We design a hand sensor that could capture the palm print and palm vein image using low-resolution web camera Both the visible and infrared images can be captured at the same time, and we do not need specialized infrared sensor to image the vein pattern The design of the device is simple and low-cost The subject can be shielded completely from the complication of undergoing two separate acquiring processes We allow subjects to position their hands freely above the sensor and they can move their hands during the acquisition process In order to obtain clear image of the palm vascular pattern, we propose a novel image enhancement technique called local-ridge-enhancement (LRE) The proposed method removes illumination error while keeping good contrast between the print/vein pattern and the background image Besides, we introduce a simple yet robust directional coding technique to encode the palm print and palm vein features in bit string representation The bit string representation offers speedy template matching and enables more effective template storage and retrieval The scores output by the palm print and palm vein experts are fused using Support Vector Machine The fusion of these features yields promising result for practical implementation