2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)最新文献_第3页

Globally-scalable Automated Target Recognition (GATR) 全球可扩展的自动目标识别

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174585

Gary Chern, A. Groener, Michael Harner, Tyler Kuhns, A. Lam, Stephen O’Neill, M. D. Pritt

{"title":"Globally-scalable Automated Target Recognition (GATR)","authors":"Gary Chern, A. Groener, Michael Harner, Tyler Kuhns, A. Lam, Stephen O’Neill, M. D. Pritt","doi":"10.1109/AIPR47015.2019.9174585","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174585","url":null,"abstract":"GATR (Globally-scalable Automated Target Recognition) is a Lockheed Martin software system for real-time object detection and classification in satellite imagery on a worldwide basis. GATR uses GPU-accelerated deep learning software to quickly search large geographic regions. On a single GPU it processes imagery at a rate of over 16 km2/sec (or more than 10 Mpixels/sec), and it requires only two hours to search the entire state of Pennsylvania for gas fracking wells. The search time scales linearly with the geographic area, and the processing rate scales linearly with the number of GPUs. GATR has a modular, cloud-based architecture that uses Maxar’s GBDX platform and provides an ATR analytic as a service. Applications include broad area search, watch boxes for monitoring ports and airfields, and site characterization. ATR is performed by deep learning models including RetinaNet and Faster R-CNN. Results are presented for the detection of aircraft and fracking wells and show that the recalls exceed 90% even in geographic regions never seen before. GATR is extensible to new targets, such as cars and ships, and it also handles radar and infrared imagery.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125091403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Sensitivity of Multiview 3D Point Cloud Reconstruction to Compression Quality and Image Feature Detectability 多视角三维点云重构对压缩质量和图像特征可检测性的敏感性

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174580

Ke Gao, Shizeng Yao, H. Aliakbarpour, S. Agarwal, G. Seetharaman, K. Palaniappan

{"title":"Sensitivity of Multiview 3D Point Cloud Reconstruction to Compression Quality and Image Feature Detectability","authors":"Ke Gao, Shizeng Yao, H. Aliakbarpour, S. Agarwal, G. Seetharaman, K. Palaniappan","doi":"10.1109/AIPR47015.2019.9174580","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174580","url":null,"abstract":"In this paper we evaluate the quality of feature detection and 3D reconstruction on a Wide Area Motion Imagery (WAMI) sequence with increasing JPEG compression ratio. Feature detection is critical for computer vision tasks such as 3D reconstruction. For some 3D reconstruction approaches, the quality of a 3D model relies upon consistent detection of the same feature points over consecutive frames in an image sequence. Since the performance of feature detectors is highly sensitive to compression artifacts, we evaluate the influence of image quality on feature detection accuracy. Many datasets (e.g. WAMI) use JPEG compression to decrease the data storage and network bandwidth utilization while attempting to preserve image quality by adaptively adjusting the compression ratio. Consequently, it is important to understand the impact of JPEG compression on the quality of feature detection in 2D space and the subsequent 3D reconstruction results. We design and perform two evaluation procedures on the WAMI sequence. We use structure tensor to detect feature points on an image sequence with increasing JPEG compression ratio (10:1, 15:1, 20:1, 30:1, 40:1, 100:1, and 150:1). Compression ratio of 10:1 is used as the baseline (groundtruth). First we compare the feature points from images of different qualities with the groundtruth features and evaluate them on pixel level in 2D space. After that, a 3D model in the form of point cloud is generated from each set of feature points and compared with the groundtruth point cloud. We provide quantitative and visualized results for the evaluation.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126537547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Object Counting using KAZE Features Under Different Lighting Conditions for Inventory Management 在不同光照条件下利用KAZE特征进行物品计数的库存管理

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174578

Teena Sharma, Astha Jain, N. Verma, S. Vasikarla

{"title":"Object Counting using KAZE Features Under Different Lighting Conditions for Inventory Management","authors":"Teena Sharma, Astha Jain, N. Verma, S. Vasikarla","doi":"10.1109/AIPR47015.2019.9174578","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174578","url":null,"abstract":"Inventory management in an automated industrial environment is the foremost requirement in order to shorten the gap between demand and supply. It comprises of object identification, localization, and counting. This paper introduces an approach for object counting in automated inventory management using KAZE features under different lighting conditions. Firstly, the prototype image and real-time inventory feed as the scene image are captured for detection of KAZE features. The detected features in the prototype image are subjected to density based scanning clustering algorithm. The KAZE features of each cluster obtained in the prototype image are mapped with the KAZE features of inventory feed. The mapped features in inventory feed are again subjected to density based scanning clustering algorithm. The clusters obtained in the inventory feed are then processed by Homography transform. Homography transform generates the predictions for object locations by projecting prototype corners in the inventory feed. The Homography transform projection results in rectangular box polygons in the inventory feed for the tentative location of prototype instances. Since there may be multiple predictions for a single object instance, the predicted object locations are integrated by density based scanning clustering algorithm to the centroids of these rectangular box polygons. It provides the exact location of prototype instances. Finally, the count is obtained. The graphical user interface for inventory management is also designed which exhibits user-friendly attributes. The proposed approach has also been compared with the previously developed approaches of object counting in automated inventory management. The experimental results state that the proposed approach outperforms the existing ones in the presence of different lighting conditions such as low-light or dim-light and bright light.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130275939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Evaluation of Generative Adversarial Network Performance Based on Direct Analysis of Generated Images 基于生成图像直接分析的生成对抗网络性能评价

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174595

Shuyue Guan, M. Loew

{"title":"Evaluation of Generative Adversarial Network Performance Based on Direct Analysis of Generated Images","authors":"Shuyue Guan, M. Loew","doi":"10.1109/AIPR47015.2019.9174595","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174595","url":null,"abstract":"Recently, a number of papers have addressed the theory and applications of the Generative Adversarial Network (GAN) in various fields of image processing. Fewer studies, however, have directly evaluated GAN outputs. Those that have been conducted focused on using classification performance and statistical metrics. In this paper, we consider a fundamental way to evaluate GANs by directly analyzing the images they generate, instead of using them as inputs to other classifiers. We consider an ideal GAN according to three aspects: 1) Creativity: non-duplication of the real images. 2) Inheritance: generated images should have the same style, which retains key features of the real images. 3) Diversity: generated images are different from each other. Based on the three aspects, we have designed the Creativity-Inheritance-Diversity (CID) index to evaluate GAN performance. We compared our proposed measures with three commonly used GAN evaluation methods: Inception Score (IS), Fréchet Inception Distance (FID) and 1-Nearest Neighbor classifier (1NNC). In addition, we discuss how the evaluation could help us deepen our understanding of GANs and improve their performance.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115801699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

SRC3: A Video Dataset for Evaluating Domain Mismatch sr3:一种评估域不匹配的视频数据集

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174589

Jonathan Sato, Chelsea Mediavilla, C. Ward, S. Parameswaran

{"title":"SRC3: A Video Dataset for Evaluating Domain Mismatch","authors":"Jonathan Sato, Chelsea Mediavilla, C. Ward, S. Parameswaran","doi":"10.1109/AIPR47015.2019.9174589","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174589","url":null,"abstract":"In this paper we introduce new video datasets to investigate the gaps between synthetic and real imagery in object detection and depth estimation. Currently, synthetic image datasets with real-world counterparts largely focus on computer vision applications for autonomous driving in unconstrained environments. The high scene complexity of such datasets pose challenges for systematic studies of domain disparities. We aim to create a set of paired datasets to study the discrepancies between the two domains in a more controlled setting. To this end, we have created Synthetic-Real Counterpart 3 (SRC3), which contains multiple datasets with varying levels of scene and object complexity. These versatile datasets span multiple environments and consist of ground-truthed, real-world, and synthetic videos generated using a gaming engine. In addition to the dataset, we present an in-depth analysis and provide comparison benchmarks of these datasets using state-of-the-art detection algorithms. Our results show contrasting performance during cross-domain testing due to differences in image quality and statistics, indicating a need for domain adapted datasets and models.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127068017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Efficient Passive Sensing Monocular Relative Depth Estimation 高效被动感知单目相对深度估计

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174573

Alex Yang, G. Scott

{"title":"Efficient Passive Sensing Monocular Relative Depth Estimation","authors":"Alex Yang, G. Scott","doi":"10.1109/AIPR47015.2019.9174573","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174573","url":null,"abstract":"We propose a method to perform monocular relative depth perception using a passive visual sensor. Specifically, the proposed method makes depth estimation with a superpixel based regression model based on features extracted by a deep convolutional neural network. We have established and conducted an analysis of the key components required to create a high-efficiency pipeline to solve the depth estimation problem with superpixel-level regression and deep learning. The key contributions of our method compared to prior works are as follows. First, we have drastically simplified the depth estimation model while attaining near state-of-the-art prediction performance, through two important optimizations: the idea of the depth estimation model is completely based on superpixels that very effectively reduces the dimensionality; additionally, we exploited the scale invariant mean squared error loss function which incorporates a pairwise term with linear time complexity. Additionally, we have developed optimizations of the superpixel feature extraction, that leverage GPU computing to achieve real-time performance (over 50fps during training) Furthermore, this model does not perform up-sampling, which avoids many issues and difficulties that one would otherwise have to deal with. To perpetuate future research in this area we have created a synchronized multiple-view depth estimation training dataset that is available to the public.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126738318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Image Representations for Occluded Face Recognition via Reference Conditioned Low-Rank projection 基于参考条件低秩投影增强遮挡人脸识别图像表征

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174567

Shibashish Sen, Manikandan Ravikiran

{"title":"Enhancing Image Representations for Occluded Face Recognition via Reference Conditioned Low-Rank projection","authors":"Shibashish Sen, Manikandan Ravikiran","doi":"10.1109/AIPR47015.2019.9174567","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174567","url":null,"abstract":"Deep learning in face recognition is widely explored in recent times due to its ability to produce state-of-the-art results and availability of large public datasets. While recent deep learning approaches involving margin loss based image representations produce 99% accuracy across benchmarks, none of these studies focus explicitly on occluded face verification. Further, in real world scenarios, there is a need for efficient methods that cater to the cases of occlusion of faces with hats, scarves, goggle or sometimes exaggerated facial expression. Moreover, with face verification gathering traction in mainstream real-time embedded applications of surveillance, the proposed approaches need to be highly accurate. In this paper, we revisit the same through a large-scale study involving multiple synthetically created goggle-occluded face datasets using multiple state-of-the-art face representations. Through this study, we identify that occlusion in faces results in non-isotropic face representations in feature space which results in a drop in performance. Therefore, we propose an approach to enhance existing face representations by learning reference conditioned Low-Rank projections (RCLP), which can create isotropic representations thereby improving face recognition. We benchmark the developed approach over synthetically goggled versions of LFW, CFP-FP, ATT, FEI, Georgia Tech and Essex University face databases with representations from ResNet-ArcFace, VGGFace, MobilefaceNet-ArcFace LightCNN resulting in a total of 100 + experiments where we achieve improvements in the accuracy-rate across all with a maximum of 4% on FEI dataset. Finally, to validate the approach in a realistic scenario, we additionally present results over our internal face verification dataset of 1k images and confirm that the proposed approach only shows positive results without degrading existing baseline performance.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126076658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Internet of Things Anomaly Detection using Machine Learning 使用机器学习的物联网异常检测

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174569

L. Njilla, Larry Pearlstein, Xin-Wen Wu, Adam Lutz, Soundararajan Ezekiel

{"title":"Internet of Things Anomaly Detection using Machine Learning","authors":"L. Njilla, Larry Pearlstein, Xin-Wen Wu, Adam Lutz, Soundararajan Ezekiel","doi":"10.1109/AIPR47015.2019.9174569","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174569","url":null,"abstract":"In recent years, an increasing number of devices are being connected to the Internet that encompasses more than just traditional devices. Internet of Things integrates real-world sensors such as smart devices or environment sensors with the Internet allowing for real}-time monitoring of conditions. IoT devices are often constrained in their resources as the sensors involved are designed for specific purposes. Due to these constraints, typical methods of intrusion and anomaly detection cannot be used. Also, due to the amount of raw input data from these sensors, detecting anomalies among the noise and other background data can be computationally intensive. A possible solution to this is by using machine learning models that are trained on both normal and abnormal behavior to detect when anomalies occur. By using techniques such as autoencoders, models can be trained that have learned normal operating conditions. In this study, we explore the use of machine learning techniques such as autoencoders to effectively handle the high dimensionality of sensor datasets while consequently learning their normal operating conditions. Autoencoders are a type of neural network which attempts to reconstruct its input data by combining two NNs, an encoder, and a decoder network. The encoder learns its input by encoding it into a lower-dimensional space while capturing the interactions and correlations between variables. In this paper, we explore the use of techniques such as autoencoders to create a lower-dimensional representation of high dimensional sensor input. Autoencoders encode the data allowing for the network to learn the interactions between parameters in normal conditions which when reconstructed with the decoder represents non-anomalous behavior. When data containing anomalies are input into the network errors will occur within the reconstruction. The error between the reconstructions can be measured using a distance function to determine if an observation is anomalous.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114990031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Implicit Land Use Mapping Using Social Media Imagery 使用社交媒体图像绘制隐含土地利用地图

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174570

Connor Greenwell, Scott Workman, Nathan Jacobs

引用次数: 3

Deep Nets Spotlight Illegal, Unreported, Unregulated (IUU) Fishing 深网聚焦非法、未报告、不管制(IUU)捕鱼

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Pub Date : 2019-10-01 DOI: 10.1109/AIPR47015.2019.9174577

Darrell L. Young

{"title":"Deep Nets Spotlight Illegal, Unreported, Unregulated (IUU) Fishing","authors":"Darrell L. Young","doi":"10.1109/AIPR47015.2019.9174577","DOIUrl":"https://doi.org/10.1109/AIPR47015.2019.9174577","url":null,"abstract":"The need for increased global surveillance and enforcement efforts to combat Illegal, Unreported, Unregulated (IUU) fishing is well known. This paper describes the current research status in developing a novel technique of associating Automated Identification System (AIS) anti-collision messages to satellite vessel detects. Each detected ship image has a wealth of information which allows development of dark ship tracking and identification. A dark ship is a ship that is not broadcasting AIS. Ships involved in illegal activities often disable their AIS transmitter to avoid detection by authorities. Dark ship tracking and identification uses a deep similarity metrics to compare current and previous observations. If any of the previous observations have an identity, e.g. a known vessel on the international IUU watch-list, then the probability of its involvement in illegal activity is increased. Additional indicators of IUU activity such as frequent flag changes are combined in a probabilistic evaluation of accumulated evidence using local laws, rules, and regulations to render IUU assessments using commercially available imagery and data sources.","PeriodicalId":167075,"journal":{"name":"2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117164645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5