2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)最新文献_第4页

PIDLNet: A Physics-Induced Deep Learning Network for Characterization of Crowd Videos PIDLNet:一个物理诱导的深度学习网络，用于描述人群视频

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663817

S. Behera, T. K. Vijay, H. M. Kausik, D. P. Dogra

{"title":"PIDLNet: A Physics-Induced Deep Learning Network for Characterization of Crowd Videos","authors":"S. Behera, T. K. Vijay, H. M. Kausik, D. P. Dogra","doi":"10.1109/AVSS52988.2021.9663817","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663817","url":null,"abstract":"Human visual perception regarding crowd gatherings can provide valuable information about behavioral movements. Empirical analysis on visual perception about orderly moving crowds has revealed that such movements are often structured in nature with relatively higher order parameter and lower entropy as compared to unstructured crowd, and vice-versa. This paper proposes a Physics-Induced Deep Learning Network (PIDLNet), a deep learning framework trained on conventional 3D convolutional features combined with physics-based features. We have computed frame-level entropy and order parameter from the motion flows extracted from the crowd videos. These features are then integrated with the 3D convolutional features at a later stage in the feature extraction pipeline to aid in the crowd characterization process. Experiments reveal that the proposed network can characterize video segments depicting crowd movements with accuracy as high as 91.63%. We have obtained overall AUC of 0.9913 on highly challenging publicly available video dataset. The method outperforms existing deep-learning frameworks and conventional crowd characterization frameworks by a notable margin.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"308 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124388685","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

Virtual Inductive Loop: Real time video analytics for vehicular access control 虚拟电感回路:车辆访问控制的实时视频分析

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663748

N. Ramanathan, Allison Beach, R. Hastings, Weihong Yin, Sima Taheri, P. Brewer, Dana Eubanks, Kyoung-Jin Park, Hongli Deng, Zhong Zhang, Donald Madden, Gang Qian, Amit Mistry, Huiping Li

{"title":"Virtual Inductive Loop: Real time video analytics for vehicular access control","authors":"N. Ramanathan, Allison Beach, R. Hastings, Weihong Yin, Sima Taheri, P. Brewer, Dana Eubanks, Kyoung-Jin Park, Hongli Deng, Zhong Zhang, Donald Madden, Gang Qian, Amit Mistry, Huiping Li","doi":"10.1109/AVSS52988.2021.9663748","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663748","url":null,"abstract":"Automated access control entails automatically detecting incoming vehicles in real-time and allowing access only to authorized vehicles. Access control systems typically adopt one or more sensors such as inductive loops, light array sensors, wireless magnetometers in detecting vehicles at access points. This paper 1 provides a detailed account on a real-time video analytics system named the “ Virtual Inductive Loop ” (VIL), that we developed as an alternative cost-efficient solution for access control. The VIL system poses precision and recall rates over 98%, performs on par with current systems in latency towards detecting event onset and further adds a suite of additional capabilities to access control systems such as vehicle classification, tailgate detection and unusual event detection. The system was tested in live conditions in different site at a Naval Facility in the United States over a two year period. The project was funded by the Office of Naval Research (#N000l4-l7-C-7030).","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125531081","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

A Sample Weighting and Score Aggregation Method for Multi-query Object Matching 多查询对象匹配的样本加权和分数聚合方法

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663848

Jangwon Lee, Gang Qian, Allison Beach

{"title":"A Sample Weighting and Score Aggregation Method for Multi-query Object Matching","authors":"Jangwon Lee, Gang Qian, Allison Beach","doi":"10.1109/AVSS52988.2021.9663848","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663848","url":null,"abstract":"In this paper, we propose a simple and effective method to properly assign weights to the query samples and compute aggregated matching scores using these weights in multi-query object matching. Multi-query object matching commonly exists in many real-life problems such as finding suspicious objects in surveillance videos. In this problem, a query object is represented by multiple samples and the matching candidates in a database are ranked according to their similarities to these query samples. In this context, query samples are not equally effective to find the target object in the database, thus one of the key challenges is how to measure the effectiveness of each query to find the correct matching object. So far, however, very little attention has been paid to address this issue. Therefore, we propose a simple but effective way, Inverse Model Frequency (IMF), to measure of matching effectiveness of query samples. Furthermore, we introduce a new score aggregation method to boost the object matching performance given multiple queries. We tested the proposed method for vehicle re-identification and image retrieval tasks. Our proposed approach achieves state-of-the-art matching accuracy on two vehicle re-identification datasets (VehicleID/VeRi-776) and two image retrieval datasets (the original & revisited Oxford/Paris). The proposed approach can seamlessly plug into many existing multi-query object matching approaches to further boost their performance with minimal effort.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127183895","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

Hazardous Events Detection in Automatic Train Doors Vicinity Using Deep Neural Networks 基于深度神经网络的列车自动门附近危险事件检测

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663863

Olivier Laurendin, S. Ambellouis, A. Fleury, Ankur Mahtani, Sanaa Chafik, Clément Strauss

引用次数: 2

D4FLY Multimodal Biometric Database: multimodal fusion evaluation envisaging on-the-move biometric-based border control D4FLY多模态生物特征数据库:多模态融合评估，设想基于移动生物特征的边境控制

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663737

Lulu Chen, Jonathan N. Boyle, A. Danelakis, J. Ferryman, Simone Ferstl, Damjan Gicic, A. Grudzien, André Howe, M. Kowalski, Krzysztof Mierzejewski, T. Theoharis

引用次数: 0

A Video Analytic System for Rail Crossing Point Protection 铁路过境点保护视频分析系统

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663781

Guangliang Zhao, Ashok Pandey, Ming-Ching Chang, Siwei Lyu

{"title":"A Video Analytic System for Rail Crossing Point Protection","authors":"Guangliang Zhao, Ashok Pandey, Ming-Ching Chang, Siwei Lyu","doi":"10.1109/AVSS52988.2021.9663781","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663781","url":null,"abstract":"With the rise of AI deep learning, video surveillance based on deep neural networks can provide real-time detection and tracking of vehicles and pedestrians. We present a video analytic system for monitoring railway crossing and providing security protection for rail intersections. Our system can automatically determine the rail-crossing gate status via visual detection and analyze traffic by detecting and tracking passing vehicles, thus to oversee a set of rail-transportation related safety events. Assuming a fixed camera view, each gate RoI can be manually annotated once for each site during system setup, and then gate status can be automatically detected afterwards. Vehicles are detected using YOLOv4 and multi-target tracking is performed using DeepSORT. Safety-related events including trespassing are continuously monitored using rule-based triggering. Experimental evaluation is performed on a Youtube rail crossing dataset as well as a private dataset. On the private dataset of 76 total minutes from 38 videos, our system can successfully detect all 56 events out of 58 annotated events. On the public dataset of 14.21 hrs of videos, it detects 58 out of 62 events.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130107814","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

Action Recognition with Domain Invariant Features of Skeleton Image 基于域不变性特征的骨骼图像动作识别

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663824

Han Chen, Yifan Jiang, Hanseok Ko

引用次数: 3

Position-aware Location Regression Network for Temporal Video Grounding 基于时间视频接地的位置感知定位回归网络

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663815

Sunoh Kim, Kimin Yun, J. Choi

引用次数: 1

A Splittable DNN-Based Object Detector for Edge-Cloud Collaborative Real-Time Video Inference 基于可分割dnn的边缘云协同实时视频推断目标检测器

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663806

Joochan Lee, Yongwoo Kim, Sungtae Moon, J. Ko

{"title":"A Splittable DNN-Based Object Detector for Edge-Cloud Collaborative Real-Time Video Inference","authors":"Joochan Lee, Yongwoo Kim, Sungtae Moon, J. Ko","doi":"10.1109/AVSS52988.2021.9663806","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663806","url":null,"abstract":"While recent advances in deep neural networks (DNNs) enabled remarkable performance on various computer vision tasks, it is challenging for edge devices to perform real-time inference of complex DNN models due to their stringent resource constraint. To enhance the inference throughput, recent studies proposed collaborative intelligence (CI) that splits DNN computation into edge and cloud platforms, mostly for simple tasks such as image classification. However, for general DNN-based object detectors with a branching architecture, CI is highly restricted because of a significant feature transmission overhead. To solve this issue, this paper proposes a splittable object detector that enables edge-cloud collaborative real-time video inference. The proposed architecture includes a feature reconstruction network that can generate multiple features required for detection using a small-sized feature from the edge-side extractor. Asymmetric scaling on the feature extractor and reconstructor further reduces the transmitted feature size and edge inference latency, while maintaining detection accuracy. The performance evaluation using Yolov5 shows that the proposed model achieves 28 fps (2.45X and 1.56X higher than edge-only and cloud-only inference, respectively), on the NVIDIA Jetson TX2 platform in WiFi environment.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116454069","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

A Multi-Stream Approach for Seizure Classification with Knowledge Distillation 基于知识精馏的多流癫痫分类方法

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663770

Jen-Cheng Hou, A. McGonigal, F. Bartolomei, M. Thonnat

引用次数: 2