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Classifying Eye-Tracking Data Using Saliency Maps

Classifying Eye-Tracking Data Using Saliency Maps

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international conference on Pattern Recognition

作者： Shafin Rahman Sejuti Rahman Omar Shahid Md. Tahmeed Abdullah Jubair Ahmed Sourov Electrical & Computer Engineering North South University Robotics & Mechatronics Engineering University of Dhaka

A plethora of research in the literature shows how human eye fixation pattern varies depending on different factors, including genetics, age, social functioning, cognitive functioning, and so on. Analysis of these variations in visual attention has already elicited two potential research avenues: 1) determining the physiological or psychological state of the subject and 2) predicting the tasks associated with the act of viewing from the recorded eye-fixation data. To this end, this paper proposes a visual saliency based novel feature extraction method for automatic and quantitative classification of eye-tracking data, which is applicable to both of the research directions. Instead of directly extracting features from the fixation data, this method employs several well-known computational models of visual attention to predict eye fixation locations as saliency maps. Comparing the saliency amplitudes, similarity and dissimilarity of saliency maps with the corresponding eye fixations maps gives an extra dimension of information which is effectively utilized to generate discriminative features to classify the eye-tracking data. Extensive experimentation using Saliency4ASD [1], Age Prediction [2], and Visual Perceptual Task [3] dataset show that our saliency-based feature can achieve superior performance, outperforming the previous state-of-the-art methods [2], [4], [5] by a considerable margin. Moreover, unlike the existing application-specific solutions, our method demonstrates performance improvement across three distinct problems from the real-life domain: Autism Spectrum Disorder screening, toddler age prediction, and human visual perceptual task classification, providing a general paradigm that utilizes the extra-information inherent in saliency maps for a more accurate classification.

关键词： Learning systems Visualization Pediatrics Psychology Predictive models Feature extraction Data models

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ARSFineTune: On-the-Fly Tuning of Vision models for Unmanned Ground Vehicles

ARSFineTune: On-the-Fly Tuning of Vision Models for Unmanned...

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international conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Masud Ahmed Zahid Hasan Abu Zaher Md Faridee Mohammad Saeid Anwar Kasthuri Jayarajah Sanjay Purushotham Suya You Nirmalya Roy University of Maryland Baltimore County USA New Jersey Institute of Technology USA DEVCOM Army Research Laboratory USA

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

the performance of semantic segmentation (SS) can degrade when the data distribution in the deployed environment is different from what the model initially learned during their training. While domain adaptation (DA) and continual learning (CL) methods have been proposed to improve performance in new or unseen domains over time, the effort required for annotating large swathes of training data during deployment is non-trivial; acquiring new data for training incurs both significant network, device memory costs, and manual effort for labeling. To address this, we propose ARSFineTune, a novel framework that actively selects the most informative regions of visuals encountered by a mobile robot for the CL network to learn from, greatly minimizing the data transfer overhead related to annotations. We first propose a proficient entropy-driven ranking mechanism to identify candidate regions and rank challenging images at the edge node. We then facilitate a cyclical feedback loop between the server and edge, continuously refining the accuracy of semantic segmentation by fine-tuning the model with minimal transferred data to/from the field deployed device. We implement ARSFineTune in a real-time setting using the robotics Operating System (ROS), where a Jackal (an unmanned ground vehicle - UGV) collaborates with the central server. through extensive experiments, we found that ARSFineTune delivers competitive performance, closely aligning with existing state-of-the-art techniques, while requiring substantially less data for fine-tuning. Specifically, with only 5% of the total labeled regions (25% challenging regions of the most 20% problematic samples) of the entire dataset for fine-tuning, ARSFineTune reaches a performance level nearly identical (≈ 97%) to the previous state-of-the-art model, boasting mIoU scores of 59.5% on the Cityscape dataset and 41% on the CAD-EdgeTune dataset, which is a challenging dataset due to varying lighting conditions over time. the reduction in

关键词： Training Performance evaluation Annotations Semantic segmentation Operating systems Image edge detection Memory management

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Parallel Network to Learn Novelty from the Known

Parallel Network to Learn Novelty from the Known

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international conference on Pattern Recognition

作者： Shuaiyuan Du Chaoyi Hong Zhiyu Pan Chen Feng Zhiguo Cao National Key Laboratory of Science and Technology on Multi-Spectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Towards multi-class novelty detection, we propose an end-to-end trainable Parallel Network (PN) using no additional data but only the training set. Our key idea is to first divide the training set into successive subtasks of pseudo-novelty detection to simulate real scenarios. We then design PN which consists of multiple branches to address the pseudo-tasks. this yields a more compact and discrminative classification space and forms a natural ensemble. In practice, we sample a random subset of the training classes as pseudo-novel and regard the extra as known. the sampling result forms a sub-task fed to one branch of PN. All training classes are equally divided as pseudo-novel to PN's branches for better data balance and model diversity. By distinguishing between the known and the diverse pseudo-novel, PN extracts the concept of novelty in a compact classification space. this provides PN with generalization ability to real novel classes which are absent during training. During online inference, this ability is further strengthened with the ensemble of PN's multiple branches. Experiments on three public datasets show our method's superiority to the mainstream methods.

关键词： Training Aggregates Training data Benchmark testing Data models Pattern recognition Anomaly detection

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Label Self-Adaption Hashing for Image Retrieval

Label Self-Adaption Hashing for Image Retrieval

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international conference on Pattern Recognition

作者： Jianglin Lu Zhihui Lai Jingxu Lin Qinghong Lin Jie Zhou Institute of Computer Mathematics and Information Technologies Kazan Federal University Kazan Russia SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China College of Computer Science and Software Engineering Shenzhen Univeristy Shenzhen China

Hashing has attracted widespread attention in image retrieval because of its fast retrieval speed and low storage cost. Compared with supervised methods, unsupervised hashing methods are more reasonable and suitable for large-scale image retrieval since it is always difficult and expensive to collect true labels of the massive data. Without label information, however, unsupervised hashing methods can not guarantee the quality of learned binary codes. To resolve this dilemma, this paper proposes a novel unsupervised hashing method called Label Self-Adaption Hashing (LSAH), which contains effective hashing function learning part and self-adaption label generation part. In the first part, we utilize anchor graph to keep the local structure of the data and introduce joint sparsity into the model to extract effective features for high-quality binary code learning. In the second part, a self-adaptive cluster label matrix is learned from the data under the assumption that the nearest neighbor points should have a large probability to be in the same cluster. therefore, the proposed LSAH can make full use of the potential discriminative information of data to guide the learning of binary codes. It is worth noting that LSAH can learn effective binary codes, hashing function and cluster labels simultaneously in a unified optimization framework. To solve the resulting optimization problem, an Augmented Lagrange Multiplier based iterative algorithm is elaborately designed. Extensive experiments on three large-scale data sets indicate the promising performance of the proposed LSAH.

关键词： Training Image resolution Image retrieval Binary codes Feature extraction Data models Data mining

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Petri Nets Tracking Control for Electro-pneumatic Systems automation 15th

Petri Nets Tracking Control for Electro-pneumatic Systems Au...

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15th international conference on Informatics in Control, automation and robotics (ICINCO)

作者： Renato Vazquez, Carlos Antonio Gomez-Castellanos, Jose Ramirez-Trevino, Antonio Tecnol Monterrey Escuela Ingn & Ciencias Av Ramon Corona 2514 Zapopan 45019 Mexico UTZMG Carretera Santa Cruz San Isidro Km 45 Tlajomulco De Zuniga 45640 Mexico CINVESTAV Unidad Guadalajara Av Bosque 1145 Zapopan 45019 Mexico

ISBN: (纸本)9783030319939;9783030319922

In this work, an output tracking control problem is introduced for electro-pneumatic systems (ENS) modeled by interpreted Petri nets (IPN). In this framework, IPN models for the most common components in ENS are present. Later, it is explained how to compute a plant model and an specification model as IPN's. the specification can allow to concurrently activate several actuators. Afterwards, an algorithm is introduced to compute the controller and the closed-loop system as IPN's. It is demonstrated that the proposed controller solves the tracking control problem, ensuring boundedness and deadlock-freeness of the closed-loop system. the resulting controller can be translated to a Ladder Diagram for its implementation with a provided algorithm. the introduced algorithms are illustrated through a case study. the aim of this work is to provide a control framework that is simple and convenient for practitioners in the field of ENS's, but at the same time is based on a formal model for analysis purposes.

关键词： Controllers

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Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning 21

Benchmarking Lane-changing Decision-making for Deep Reinforc...

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Proceedings of the 7th international conference on robotics and Artificial Intelligence

作者： Junjie Wang Qichao Zhang Dongbin Zhao The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences China and College of Artificial Intelligence University of Chinese Academy of Sciences China

ISBN: (纸本)9781450385855

the development of autonomous driving has attracted extensive attention in recent years, and it is essential to evaluate the performance of autonomous driving. However, testing on the road is expensive and inefficient. Virtual testing is the primary way to validate and verify self-driving cars, and the basis of virtual testing is to build simulation scenarios. In this paper, we propose a training, testing, and evaluation pipeline for the lane-changing task from the perspective of deep reinforcement learning. First, we design lane change scenarios for training and testing, where the test scenarios include stochastic and deterministic parts. then, we deploy a set of benchmarks consisting of learning and non-learning approaches. We train several state-of-the-art deep reinforcement learning methods in the designed training scenarios and provide the benchmark metrics evaluation results of the trained models in the test scenarios. the designed lane-changing scenarios and benchmarks are both opened to provide a consistent experimental environment for the lane-changing task1 .

关键词： Autonomous driving Reinforcement learning Lane change scenarios

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Exoskeleton for Hemiplegic Patients: Mechatronic Approach to Move One Disabled Lower Limb with Posture Recognition Neural Network for More Safety 8

Exoskeleton for Hemiplegic Patients: Mechatronic Approach to...

引用

8th international conference on Control, Mechatronics and automation, ICCMA 2020

作者： Hamza, Alaoui Moutacalli, Mohamed Tarik Chebak, Ahmed University of Quebec in Rimouski Electrical and Computer Engineering Department Lèvis Canada

ISBN: (纸本)9781728192109

the number of people suffering from hemiplegia is growing each year. this lower limb disability affects all the aspects of their lives by taking away their autonomy. this implicates their close relatives, as well as the health system to provide the necessary care they need. the integration of exoskeletons in the medical field became a promising solution to resolve this issue. this paper presents an exoskeleton designed to help hemiplegic people get back the sensation and ability of normal walking, with the integration of posture recognition using a simple RGB camera to insure safety. three step models have been created. the first step allows a simple forward movement of the leg. the second method is designed to overcome some obstacles in the patient path, and finally the third step model gives the patient total control over the device. Each of the control methods was designed to offer a solution to the challenges that the patients may face during the walking process. © 2020 IEEE.

关键词： Exoskeleton (robotics)

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Deep & Attention: A Self-Attention based Neural Network for Remaining Useful Lifetime Predictions

Deep & Attention: A Self-Attention based Neural Network for ...

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Mechatronics and robotics Engineering (ICMRE), international conference on

作者： Yuanjun Liu Xingang Wang Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665414890

ISBN: (纸本)9781665430531

the remaining useful lifetime (RUL) of assets plays a critical role in machine prognostics and health management (PHM). Accurate RUL predictions can reduce losses caused by equipment faults. Most existing data-driven PHM methods rely on long short-term memory (LSTM) networks to model the relationship of time series data and RUL. However, because of the sequential nature of LSTM, it is not conducive to parallel computing. Herein, we propose the Deep & Attention Network, which uses a combination of convolutional neural networks and Attention methodologies instead of LSTM. In the proposed Deep & Attention Network, the Attention component models the temporal property, while the Deep component learns the effect of noise data. Experiments on NASA's Commercial Modular Aero- Propulsion System Simulation datasets demonstrate that the proposed network achieves a level of performance similar to that of other state-of-the-art RUL prediction models. Moreover, compared with LSTM-based methods, our Self-Attention-based method is conducive to parallel computing.

关键词： Computational modeling Time series analysis Parallel processing Data models Timing Systems simulation Prognostics and health management

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Sim2Real2Sim: Bridging the Gap Between Simulation and Real-World in Flexible Object Manipulation 4

Sim2Real2Sim: Bridging the Gap Between Simulation and Real-W...

引用

4th IEEE international conference on Robotic Computing (IEEE IRC)

作者： Chang, Peng Padir, Taskin Northeastern Univ Coll Engn Boston MA 02115 USA Northeastern Univ Inst Experiential Robot Boston MA 02115 USA

ISBN: (纸本)9781728152370

this paper addresses a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim) to bridge the gap between simulation and real-world, and automate a flexible object manipulation task. this strategy consists of three steps: (1) using the rough environment with the estimated models to develop the methods to complete the manipulation task in the simulation;(2) applying the methods from simulation to real-world and comparing their performance;(3) updating the models and methods in simulation based on the differences between the real world and the simulation. the Plug Task from the 2015 DARPA robotics Challenge Finals is chosen to evaluate our Sim2Real2Sim strategy. A new identification approach for building the model of the linear flexible objects is derived from real-world to simulation. the automation of the DRC plug task in both simulation and real-world proves the success of the Sim2Real2Sim strategy. Numerical experiments are implemented to validate the simulated model.

关键词： Flexible object manipulation Modeling of flexible objects Robot kinematics and dynamics Robot simulation Simulation to Real (Sim2Real) Task automation

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Research of Control Systems for Robotic Spatial Planning Platforms

Research of Control Systems for Robotic Spatial Planning Pla...

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international conference on Modern Electrical and Energy Systems (MEES)

作者： Yurii Lebedenko Oksana Polyvoda Anatolii Derkachenko Yevhenii Modlo Svitlana Demishonkova Yurii Pylypenko Information and Computer Technologies Department Kyiv National University of Technologies and Design Kyiv Ukraine Automation Robotics and Mechatronics Department Kherson National Technical University Kherson Ukraine Technological Institute State University of Economics and Technology Kryvyi Rih Ukraine Department of Computer Engineering and Electromechanics Kyiv National University of Technologies and Design Kyiv Ukraine

ISBN: (纸本)9798350346848

the work is devoted to the study of the control system of the moving platform of the manipulative robot. the purpose of the work is the development and research of an instrumental complex of control, analysis and modeling of the operation of positioning platforms based on a spherical mechanism for use in automated industrial systems. the state of use of multi-link positioning platforms in various industries was analyzed and their classification was provided, the main areas of use were determined. A comparative analysis of mathematical methods for describing the kinematic properties of positioning platforms was performed, namely: the closed vector contour method, the coordinate transformation method. A model with an analytical apparatus for controlling the drive links of the spherical mechanism according to the given scenarios of the orientation of the positioning platform was developed, as well as a mathematical model of inverse kinematics was made, the working space was modeled, and the service angles were determined, and a calculation and real-life experiment was conducted. For the first time, the work uses the dependencies of analytical geometry to implement algorithms for controlling a spherical mechanism by inverse kinematics, and offers software and hardware that combines visual modeling tools with real-time data transmission to a controller that controls the drivers of a spherical mechanism by direct kinematics. the result of the field experiment is the testing of the proposed control algorithm on the manufactured manipulator mechanism on the most common movements of the platform of the spherical mechanism and its exit to the extreme positions in order to determine the working space and the values of the service angles. A system of remote control of the spherical mechanism at the hardware level with an interactive interface and tracking of the positioning target has been implemented.

关键词： Analytical models Target tracking Robot kinematics Software algorithms Kinematics Aerospace electronics Control systems

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