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Multimodal information fusion for urban scene understanding

MACHINE VISION and applications 2016年第3期27卷 331-349页

作者： Xu, Philippe Davoine, Franck Bordes, Jean-Baptiste Zhao, Huijing Denoeux, Thierry Univ Technol Compiegne CNRS UMR 7253 Heudiasyc BP 20529 F-60205 Compiegne France Peking Univ Key Lab Machine Percept MOE CNRS LIAMA Beijing 100871 Peoples R China

This paper addresses the problem of scene understanding for driver assistance systems. To recognize the large number of objects that may be found on the road, several sensors and decision algorithms have to be used. The proposed approach is based on the representation of all available information in over-segmented image regions. The main novelty of the framework is its capability to incorporate new classes of objects and to include new sensors or detection methods while remaining robust to sensor failures. Several classes such as ground, vegetation or sky are considered, as well as three different sensors. The approach was evaluated on real publicly available urban driving scene data.

关键词： Information fusion Driving scene understanding Theory of belief functions Intelligent vehicles Dempster-Shafer theory Evidence theory

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Skin Subspace Color Modeling for Daytime and Nighttime Group Activity Recognition in Confined Operational Spaces 25

Skin Subspace Color Modeling for Daytime and Nighttime Group...

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conference on Signal Processing, sensor/Information fusion, and Target Recognition XXV

作者： Shirkhodaie, Amir Poshtyar, Azin Chan, Alex Hu, Shuowen Tennessee State Univ Ctr Excellence Battlefield Sensor Fus Dept Mech & Mfg Engn 3500 John Merritt Blvd Nashville TN 37209 USA US Army Res Lab Image Proc Branch 2800 Powder Mill Rd Adelphi MD 20783 USA

ISBN: (纸本)9781510600836

In many military and homeland security persistent surveillance applications, accurate detection of different skin colors in varying observability and illumination conditions is a valuable capability for video analytics. One of those applications is In-Vehicle Group Activity (IVGA) recognition, in which significant changes in observability and illumination may occur during the course of a specific human group activity of interest. Most of the existing skin color detection algorithms, however, are unable to perform satisfactorily in confined operational spaces with partial observability and occultation, as well as under diverse and changing levels of illumination intensity, reflection, and diffraction. In this paper, we investigate the salient features of ten popular color spaces for skin subspace color modeling. More specifically, we examine the advantages and disadvantages of each of these color spaces, as well as the stability and suitability of their features in differentiating skin colors under various illumination conditions. The salient features of different color subspaces are methodically discussed and graphically presented. Furthermore, we present robust and adaptive algorithms for skin color detection based on this analysis. Through examples, we demonstrate the efficiency and effectiveness of these new color skin detection algorithms and discuss their applicability for skin detection in IVGA recognition applications.

关键词： Skin Color Space Modeling Daytime and Nighttime In-Vehicle Group Activity Recognition Confined Operational Spaces

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Constrained Robust Submodular sensor Selection with applications to Multistatic Sonar Arrays 19

Constrained Robust Submodular Sensor Selection with Applicat...

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19th International conference on Information fusion (fusion)

作者： Powers, Thomas Bilmes, Jeff Krout, David W. Atlas, Les Univ Washington Dept Elect Engn Seattle WA 98195 USA Univ Washington Appl Phys Lab Seattle WA 98105 USA

ISBN: (纸本)9780996452748

We develop a framework to select a subset of sensors from a field in which the sensors have an ingrained independence structure. Given an arbitrary independence pattern, we construct a graph that denotes pairwise independence between sensors, which means those sensors may operate simultaneously. The set of all fully-connected subgraphs (cliques) of this independence graph forms the independent sets of a matroid over which we maximize the minimum of a set of submodular objective functions. We propose a novel algorithm called MatSat that exploits submodularity and, as a result, returns a near-optimal solution with approximation guarantees that are within a small factor of the average-case scenario. We apply this framework to ping sequence optimization for active multistatic sonar arrays by maximizing sensor coverage and derive lower bounds for minimum probability of detection for a fractional number of targets. In these ping sequence optimization simulations, MatSat exceeds the fractional lower bounds and reaches near-optimal performance.

关键词： Approximation algorithms

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Fault Detection Prediction Analysis of Multi-sensor Data fusion Architecture and Isolation Using Pseudo sensor Enhancement Method (PSEM) 7

Fault Detection Prediction Analysis of Multi-Sensor Data Fus...

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7th International conference on Intelligent Systems, Modelling and Simulation (ISMS)

作者： Madany, Yasser M. El-Badawy, El-Sayed A. Soliman, Adel M. Alexandria Univ Commun & Elect Dept Alexandria Egypt

ISBN: (纸本)9781509006656

The recent technology of intelligent systems is using multiple sensors to assess real-time information of the internal system states and the environmental operating conditions. In the case of sensor (s) transient faults, the disconnection of the readings causes inappropriate actions. So the need for sensor fault detecting and estimating multiple sensor faults becomes an important issue to optimize and enhance the system performance. In this paper, fault detection prediction analysis of multi-sensor architecture is introduced to study different number of sensors with different arrangement architectures and to detect transient faults in each sensor. Then, a new proposed pseudo sensor enhancement method (PSEM) is presented and investigated to isolate and compensate for the transient faults. The simulation results of different sensor architecture configurations without and with applying the PSEM have been obtained and analyzed to demonstrate the enhancement in operational performance of the proposed method to meet the requirements for multiple sensor intelligent systems.

关键词： Multiple sensors fault detection prediction fault isolation pseudo sensor enhancement method (PSEM) intelligent system applications

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Experimental Study on Using Visual Odometry for Navigation in Outdoor GPS-Denied Environments 12

Experimental Study on Using Visual Odometry for Navigation i...

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Symposium of Mechatronic and Embedded Technologies in Intelligent Transporation Systems at the 12th IEEE/ASME International conference on Mechatronic and Embedded Systems and applications (MESA)

作者： Sharifi, Mostafa Chen, XiaoQi Pretty, Christopher G. Univ Canterbury Dept Mech Engn Christchurch New Zealand

ISBN: (纸本)9781509061907

This paper presents an experimental study on utilizing visual odometry (VO) for pose estimation of mobile robots in outdoor GPS-denied environments. The experiments have been done using two different stereo VO algorithms implemented in Robot Operating System (ROS). The results have been compared with ground truth and drift has been identified. sensor integration has been successfully used to minimize the drift. Challenges and possible solutions have been presented for future research and development.

关键词： VO drift minimization sensor fusion GPS denied environment motion estimation

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The effects of camera jitter for background subtraction algorithms on fused infrared-visible video streams 12

The effects of camera jitter for background subtraction algo...

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conference on Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII

作者： Becker, Stefan Scherer-Negenborn, Norbert Thakkar, Pooja Huebner, Wolfgang Arens, Michael Fraunhofer Inst Optron Syst Technol & Image Explo Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (纸本)9781510603943;9781510603950

This paper is a continuation of the work of Becker et al.(1) In their work, they analyzed the robustness of various background subtraction algorithms on fused video streams originating from visible and infrared cameras. In order to cover a broader range of background subtraction applications, we show the effects of fusing infrared-visible video streams from vibrating cameras on a large set of background subtraction algorithms. The effectiveness is quantitatively analyzed on recorded data of a typical outdoor sequence with a fine-grained and accurate annotation of the images. Thereby, we identify approaches which can benefit from fused sensor signals with camera jitter. Finally conclusions on what fusion strategies should be preferred under such conditions are given.

关键词： Image fusion Background Subtraction Camera Jitter Multi-Spectral Imaging

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Performance evaluation on contour extraction using Hough transform and RANSAC for multi-sensor data fusion applications in industrial food inspection

Performance evaluation on contour extraction using Hough tra...

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IEEE Signal Processing algorithms, architectures, Arrangements, and applications conference

作者： M. Kroger W. Sauer-Greff R. Urbansky M. Lorang M. Siegrist Chair for Communications Engeneering University of Kaiserslautern WIPOTEC GmbH

ISBN: (纸本)9781509026616

For multi-sensor data fusion applications the accurate alignment of different sensor data is essential for the proper combination of matching features. In food inspection system the boxing often is in a rectangular shape. This knowledge can be used to rectify the image data, an important step in the alignment stage. In case of low contrast between boxing and background, the detected contour may differ significantly from the actual values. In this paper the performance of the Hough transform and the RANdom SAmple Consensus (RANSAC)-algorithm are evaluated relating to the correct extraction of the boxing contour out of contour data distorted by position errors of the outer shape. The evaluation results indicate the superiority of the RANSAC algorithm with respect to scalability, robustness and execution time.

关键词： Hough transform Food inspection Edge detection boxing multi-sensor data fusion sensor Data Performance Evaluation location error contour image data execution time

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IR-UWB Detection and fusion Strategies using Multiple Detector Types

IR-UWB Detection and Fusion Strategies using Multiple Detect...

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IEEE Wireless Communications and Networking conference (WCNC)

作者： Yajnanarayana, Vijaya Dwivedi, Satyam Handel, Peter KTH Royal Inst Technol Dept Signal Proc ACCESS Linnaeus Ctr Stockholm Sweden

ISBN: (纸本)9781467398145

Optimal detection of ultra wideband (UWB) pulses in a UWB transceiver employing multiple detector types is proposed and analyzed in this paper. To enable the transceiver to be used for multiple applications, the designers have different types of detectors such as energy detector, amplitude detector, etc., built in to a single transceiver architecture. We propose several fusion techniques for fusing decisions made by individual IR-UWB detectors. In order to get early insight into theoretical achievable performance of these fusion techniques, we assess the performance of these fusion techniques for commonly used detector types like matched filter, energy detector and amplitude detector under Gaussian assumption. These are valid for ultra short distance communication and in UWB systems operating in millimeter wave (mmwave) band with high directivity gain. In this paper, we utilize the performance equations of different detectors, to device distinct fusion algorithms. We show that the performance can be improved approximately by 4 dB in terms of signal to noise ratio (SNR) for high probability of detection of a UWB signal (> 95%), by fusing decisions from multiple detector types compared to a standalone energy detector, in a practical scenario.

关键词： Ultra Wideband (UWB) UWB ranging sensor Networks Time of Arrival (TOA) Neyman-Pearson test

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Constructing a Deep Regression Model Utilizing Cascaded Sparse Autoencoders and Stochastic Gradient Descent 15

Constructing a Deep Regression Model Utilizing Cascaded Spar...

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15th IEEE International conference on Machine Learning and applications (ICMLA)

作者： Moussavi-Khalkhali, Arezou Jamshidi, Mo Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA

ISBN: (纸本)9781509061679

This paper discusses utilizing sparse autoencoders for building regression models in order to predict real-valued timeseries data. The focus of this research is on exploiting and learning from the determining features of continuous data via stacked autoencoders, thus increasing the prediction accuracy of regression method. Architecture comprising different layers of sparse autoencoders, where each level of autoencoders are trained based on the standard (typical) method are implemented and analyzed. In order to enhance the accuracy of the typical model of training autoencoders, cascaded model that benefits from the fusion of low-and high-level features is proposed. The objective is to improve vehicular traffic flow forecasting, since this area is a research field in progress that impacts daily life. The SGD algorithm at the top level of deep architectures serves as the regression method. Evaluations are based on the precision accuracy of the algorithms applied to forecasting the traffic flow of a location down a key highway using the historical traffic data of several locations ahead in the Twin Cities Metro area of Minneapolis.

关键词： traffic flow prediciton sparse autoencoders cascaded sparse autoencoders feature fusion deep regression model

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Indoor SLAM for Micro Aerial Vehicles Control using Monocular Camera and sensor fusion

Indoor SLAM for Micro Aerial Vehicles Control using Monocula...

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IEEE International conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Garcia, Sergio Elena Lopez, M. Barea, Rafael Bergasa, Luis M. Gomez, Alejandro Molinos, Eduardo J. Univ Alcala Dept Elect Alcala De Henares Madrid Spain

ISBN: (纸本)9781509022557

This paper represents research in progress in Simultaneous Localization and Mapping (SLAM) for Micro Aerial Vehicles (MAVs) in the context of rescue and/or recognition navigation tasks in indoor environments. In this kind of applications, the MAV must rely on its own onboard sensors to autonomously navigate in unknown, hostile and GPS denied environments, such as ruined or semi-demolished buildings. This article aims to investigate a SLAM technique that fuses visual information and measurements from the inertial measurement unit (IMU), to robustly obtain the 6DOF pose estimation of a MAV within a local map of the environment. The monocular visual SLAM algorithm along with the IMU calculate the pose estimation through an Extended Kalman Filter (EKF). The system consists of a low-cost commercial drone and a remote control unit to computationally afford the SLAM algorithms using a distributed node system based on ROS (Robot Operating System). Some experimental results show how sensor fusion improves the position estimation and the obtained map under different test conditions.

关键词： micro aerial vehicles indoor navigation sensor fusion simultaneous localization and mapping robot operating system

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