Accurate and up-to-date 3D maps, often represented as point clouds, are crucial for autonomous vehicles. Crowd-sourcing has emerged as a low-cost and scalable approach for collecting mapping data utilizing widely available dashcams and other sensing devices. However, it is still a non-trivial task to utilize crowdsourced data, such as dashcam images and video, to efficiently create or update high-quality point clouds using technologies like Structure from Motion (SfM). This study assesses and compares different image matching options available in open-source SfM software, analyzing their applicability and limitations for mapping urban scenes in different practical scenarios. Furthermore, the study analyzes the impact of various camera setups (i.e., the number of cameras and their placement) and weather conditions on the quality of the generated 3D point clouds in terms of completeness and accuracy. Based on these analyses, our study provides guidelines for creating more accurate point clouds.
{"title":"Structure from Motion-Based Mapping for Autonomous Driving: Practice and Experience","authors":"Aziza Zhanabatyrova, Clayton Souza Leite, Yu Xiao","doi":"10.1145/3631533","DOIUrl":"https://doi.org/10.1145/3631533","url":null,"abstract":"Accurate and up-to-date 3D maps, often represented as point clouds, are crucial for autonomous vehicles. Crowd-sourcing has emerged as a low-cost and scalable approach for collecting mapping data utilizing widely available dashcams and other sensing devices. However, it is still a non-trivial task to utilize crowdsourced data, such as dashcam images and video, to efficiently create or update high-quality point clouds using technologies like Structure from Motion (SfM). This study assesses and compares different image matching options available in open-source SfM software, analyzing their applicability and limitations for mapping urban scenes in different practical scenarios. Furthermore, the study analyzes the impact of various camera setups (i.e., the number of cameras and their placement) and weather conditions on the quality of the generated 3D point clouds in terms of completeness and accuracy. Based on these analyses, our study provides guidelines for creating more accurate point clouds.","PeriodicalId":500855,"journal":{"name":"ACM transactions on the internet of things","volume":"2017 41","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135635985","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}
Tianben Wang, Zhangben Li, Honghao Yan, Xiantao Liu, Boqin Liu, Shengjie Li, Zhongyu Ma, Jin Hu, Daqing Zhang, Tao Gu
Indoor intrusion detection is a critical task for home security. Previous works in intrusion detection suffer from the problems such as blind spots in non-line-of-sight (NLOS) areas, restricted device locations, massive offline training required, and privacy concern. In this paper, we design and implement an omnidirectional indoor intrusion detection system, named AudioGuard , using only a pair of speaker and microphone. AudioGuard is able to detect both line-of-sight (LOS) and NLOS intrusions. Our observation of acoustic signal propagation in an indoor environment shows that there exist abundant multipath reflections and human movement introduces Doppler shift in echo signals. We hence capture periodical Doppler shift caused by intruder's walking motion to detect intrusion. Specifically, we first extract the Doppler shift embedded in echo signals, we then propose a periodicity polarization method to cancel out the impact of the change of radial angle and the distance on periodicity of Doppler shift. Finally, we detect intrusion by measuring periodicity of Doppler shift over time. Extensive experiments show that AudioGuard achieves a miss report rate of 0% and 1.75% for LOS and NLOS intrusion, respectively, and a false alarm rate of 4.17%.
{"title":"AudioGuard: Omnidirectional Indoor Intrusion Detection Using Audio Device","authors":"Tianben Wang, Zhangben Li, Honghao Yan, Xiantao Liu, Boqin Liu, Shengjie Li, Zhongyu Ma, Jin Hu, Daqing Zhang, Tao Gu","doi":"10.1145/3625305","DOIUrl":"https://doi.org/10.1145/3625305","url":null,"abstract":"Indoor intrusion detection is a critical task for home security. Previous works in intrusion detection suffer from the problems such as blind spots in non-line-of-sight (NLOS) areas, restricted device locations, massive offline training required, and privacy concern. In this paper, we design and implement an omnidirectional indoor intrusion detection system, named AudioGuard , using only a pair of speaker and microphone. AudioGuard is able to detect both line-of-sight (LOS) and NLOS intrusions. Our observation of acoustic signal propagation in an indoor environment shows that there exist abundant multipath reflections and human movement introduces Doppler shift in echo signals. We hence capture periodical Doppler shift caused by intruder's walking motion to detect intrusion. Specifically, we first extract the Doppler shift embedded in echo signals, we then propose a periodicity polarization method to cancel out the impact of the change of radial angle and the distance on periodicity of Doppler shift. Finally, we detect intrusion by measuring periodicity of Doppler shift over time. Extensive experiments show that AudioGuard achieves a miss report rate of 0% and 1.75% for LOS and NLOS intrusion, respectively, and a false alarm rate of 4.17%.","PeriodicalId":500855,"journal":{"name":"ACM transactions on the internet of things","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135538967","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}
Machine learning-based techniques have proven to be effective in IoT network behavioral inference. Existing works developed data-driven models based on features from network packets and/or flows, but mainly in a static and ad-hoc manner, without adequately quantifying their gains versus costs. In this paper, we develop a generic architecture that comprises two distinct inference modules in tandem, which begins with IoT network behavior classification followed by continuous monitoring. In contrast to prior relevant works, our generic architecture flexibly accounts for various traffic features, modeling algorithms, and inference strategies. We argue quantitative metrics are required to systematically compare and efficiently select various traffic features for IoT traffic inference. This paper makes three contributions. (1) For IoT behavior classification, we identify four metrics, namely cost, accuracy, availability, and frequency, that allow us to characterize and quantify the efficacy of seven sets of packet-based and flow-based traffic features, each resulting in a specialized model. By experimenting with traffic traces of 25 IoT devices collected from our testbed, we demonstrate that specialized-view models can be superior to a single combined-view model trained on a plurality of features by accuracy and cost. We also develop an optimization problem that selects the best set of specialized models for a multi-view classification; (2) For monitoring the expected IoT behaviors, we develop a progressive system consisting of one-class clustering models (per IoT class) at three levels of granularity. We develop an outlier detection technique on top of the convex hull algorithm to form custom-shape boundaries for the one-class models. We show how progression helps with computing costs and the explainability of detecting anomalies; and, (3) We evaluate the efficacy of our optimally-selected classifiers versus the superset of specialized classifiers by applying them to our IoT traffic traces. We demonstrate how the optimal set can reduce the processing cost by a factor of six with insignificant impacts on the classification accuracy. Also, we apply our monitoring models to a public IoT dataset of benign and attack traces and show they yield an average true positive rate of 94% and a false positive rate of 5%. Finally, we publicly release our data (training and testing instances of classification and monitoring tasks) and code for convex hull-based one-class models.
{"title":"Efficient IoT Traffic Inference: from Multi-View Classification to Progressive Monitoring","authors":"Arman Pashamokhtari, Gustavo Batista, Hassan Habibi Gharakheili","doi":"10.1145/3625306","DOIUrl":"https://doi.org/10.1145/3625306","url":null,"abstract":"Machine learning-based techniques have proven to be effective in IoT network behavioral inference. Existing works developed data-driven models based on features from network packets and/or flows, but mainly in a static and ad-hoc manner, without adequately quantifying their gains versus costs. In this paper, we develop a generic architecture that comprises two distinct inference modules in tandem, which begins with IoT network behavior classification followed by continuous monitoring. In contrast to prior relevant works, our generic architecture flexibly accounts for various traffic features, modeling algorithms, and inference strategies. We argue quantitative metrics are required to systematically compare and efficiently select various traffic features for IoT traffic inference. This paper makes three contributions. (1) For IoT behavior classification, we identify four metrics, namely cost, accuracy, availability, and frequency, that allow us to characterize and quantify the efficacy of seven sets of packet-based and flow-based traffic features, each resulting in a specialized model. By experimenting with traffic traces of 25 IoT devices collected from our testbed, we demonstrate that specialized-view models can be superior to a single combined-view model trained on a plurality of features by accuracy and cost. We also develop an optimization problem that selects the best set of specialized models for a multi-view classification; (2) For monitoring the expected IoT behaviors, we develop a progressive system consisting of one-class clustering models (per IoT class) at three levels of granularity. We develop an outlier detection technique on top of the convex hull algorithm to form custom-shape boundaries for the one-class models. We show how progression helps with computing costs and the explainability of detecting anomalies; and, (3) We evaluate the efficacy of our optimally-selected classifiers versus the superset of specialized classifiers by applying them to our IoT traffic traces. We demonstrate how the optimal set can reduce the processing cost by a factor of six with insignificant impacts on the classification accuracy. Also, we apply our monitoring models to a public IoT dataset of benign and attack traces and show they yield an average true positive rate of 94% and a false positive rate of 5%. Finally, we publicly release our data (training and testing instances of classification and monitoring tasks) and code for convex hull-based one-class models.","PeriodicalId":500855,"journal":{"name":"ACM transactions on the internet of things","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135925506","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}
Honghong Chen, Xinyu Han, Zhanjun Hao, Hao Yan, Jie Yang
Fatigue driving is the leading cause of severe traffic accidents which is considered as an important point of the research. Although a precise definition of fatigue is lacking, it is possible to detect the physiological characteristics of the human body to determine whether a person is fatigue, such as head shaking, yawning, and a significant drop in breathing. In our study, fatigue actions were collected firstly, and then the different micro-Doppler characteristics produced by human activity were used to classify and recognize the fatigue action using the Fine-tuning convolution neural network (FT-CNN) model. The collected signals in the breathing mode were preprocessed to judge whether the person is fatigued according to the estimated value of respiratory rate. Data in different environments were collected to verify the proposed method. Our results showed that the accuracy of fatigue detection can reach 91.8% in the laboratory environment and 87.3% in realistic scenarios.
{"title":"Non-Contact Monitoring of Fatigue Driving Using FMCW Millimeter Wave Radar","authors":"Honghong Chen, Xinyu Han, Zhanjun Hao, Hao Yan, Jie Yang","doi":"10.1145/3614442","DOIUrl":"https://doi.org/10.1145/3614442","url":null,"abstract":"Fatigue driving is the leading cause of severe traffic accidents which is considered as an important point of the research. Although a precise definition of fatigue is lacking, it is possible to detect the physiological characteristics of the human body to determine whether a person is fatigue, such as head shaking, yawning, and a significant drop in breathing. In our study, fatigue actions were collected firstly, and then the different micro-Doppler characteristics produced by human activity were used to classify and recognize the fatigue action using the Fine-tuning convolution neural network (FT-CNN) model. The collected signals in the breathing mode were preprocessed to judge whether the person is fatigued according to the estimated value of respiratory rate. Data in different environments were collected to verify the proposed method. Our results showed that the accuracy of fatigue detection can reach 91.8% in the laboratory environment and 87.3% in realistic scenarios.","PeriodicalId":500855,"journal":{"name":"ACM transactions on the internet of things","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135307099","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}
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