2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)最新文献_第8页

Simple background subtraction constraint for weakly supervised background subtraction network 弱监督背景相减网络的简单背景相减约束

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909896

T. Minematsu, Atsushi Shimada, R. Taniguchi

{"title":"Simple background subtraction constraint for weakly supervised background subtraction network","authors":"T. Minematsu, Atsushi Shimada, R. Taniguchi","doi":"10.1109/AVSS.2019.8909896","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909896","url":null,"abstract":"Recently, background subtraction based on deep convolutional neural networks has demonstrated excellent performance in change detection tasks. However, most of the reported approaches require pixel-level label images for training the networks. To reduce the cost of rendering pixel-level annotation data, weakly supervised learning approaches using frame-level labels have been proposed. These labels indicate if a target class is present. Frame-level supervised learning is challenging because we cannot use location information for training the networks. Therefore, some constraints are introduced for guiding foreground locations. Previous works exploit prior information on foreground sizes and shapes. In this work, we propose two constraints for weakly supervised background subtraction networks. Our constraints use binary mask images generated by simple background subtraction. Unlike previous works, our approach does not require prior information on foreground sizes and shapes. Moreover, our constraints are more suitable for change detection tasks. We also present an experiment verifying that our constraints can improve foreground detection accuracy compared to other methods, which do not include them.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115484073","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: 11

Identification of Partially Occluded Pharmaceutical Blister Packages 部分封闭药品吸塑包装的识别

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909890

Sheng-Luen Chung, Chih-Fang Chen, G. Hsu, Shen-Te Wu

{"title":"Identification of Partially Occluded Pharmaceutical Blister Packages","authors":"Sheng-Luen Chung, Chih-Fang Chen, G. Hsu, Shen-Te Wu","doi":"10.1109/AVSS.2019.8909890","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909890","url":null,"abstract":"Medical dispensing refers to the in-office preparation and delivery of prescription drugs, which is mostly dispensed by the units of blister packages. The objective of the study is to design an image-based blister package identification solution, which is capable of identifying a fetched drug based on a pair of the two opposite camera images of the hand-held drug. To this aim, this paper proposes a deep learning based Hand-held Blister Identification network (HBIN) to identify partially occluded blister packages present in arbitrary positions and orientation with possibly cluttered backgrounds. The proposed HBIN is a two-stage network that contains Blister cropping network (BCN) followed by RTT identification network (RIN). The BCN subnetwork, an image to image translation deep learning network, is to crop both side contours of the hand-held drug, before the pair of cropped contours can be juxtaposed as a fixed sized and fixed orientation RTT (rectified two-sides template) for final identification in the RIN sub-network. A blister package dataset containing a total of 30,394 images based on 230 types, typically found in hospital dispensing stations, have been collected and labeled. With extensive test, the accuracy of the primitive primitive HBIN attains an F-score of more than 94.33% for testing data from similar backgrounds and an F-score of 79.80% for dissimilar backgrounds. Although still a prototype, the preliminary results show the feasibility of identifying blister packages during retrieval process without resorting to bar codes nor RFID tags.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129301704","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

Future Frame Prediction Using Convolutional VRNN for Anomaly Detection 基于卷积VRNN的未来帧预测异常检测

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909850

Yiwei Lu, Mahesh Kumar Krishna Reddy, Seyed shahabeddin Nabavi, Yang Wang

引用次数: 71

VikingDet: A Real-time Person and Face Detector for Surveillance Cameras VikingDet:用于监控摄像头的实时人和人脸检测器

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909901

Zhongxia Xiong, Ziying Yao, Yalong Ma, Xinkai Wu

{"title":"VikingDet: A Real-time Person and Face Detector for Surveillance Cameras","authors":"Zhongxia Xiong, Ziying Yao, Yalong Ma, Xinkai Wu","doi":"10.1109/AVSS.2019.8909901","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909901","url":null,"abstract":"In this paper, we propose a novel one-stage detector that can simultaneously detect both pedestrians and their faces. The framework is named as VikingDet for its simple but effective two-headed architecture. To tackle the challenges of person and face detection especially under surveillance cameras (e.g. low data quality, complex environments, requirements for efficiency, etc.), we make contributions in the following several aspects: 1) integrating both person and face detection into one network which current leading object detection algorithms are seldomly able to handle; 2) emphasizing detection in low-quality images. we introduce multiple thresholds for matching different sized positive samples, and set proper hyper-parameters, hence our VikingDet is able to locate small objects in surveillance cameras even of low-quality; 3) introducing a training strategy to utilize datasets on hand. Since most available public datasets annotate only people without their faces or faces without bodies, we use multi-step training and an integrated loss function to train VikingDet with these partly annotated data. As a consequence, our detector achieves satisfactory performances in several relative benchmarks with a speed at more than 60 FPS on NVIDIA TITAN X GPU, and can be further deployed on an embedded device such as NVIDIA Jetson TX1 or TX2 with a real-time speed of over 28 FPS.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121393408","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

Efficient Violence Detection Using 3D Convolutional Neural Networks 基于三维卷积神经网络的高效暴力检测

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909883

Ji Li, Xinghao Jiang, Tanfeng Sun, Ke Xu

引用次数: 38

Does Deep Super-Resolution Enhance UAV Detection? 深度超分辨率能增强无人机探测能力吗?

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909865

Vasileios Magoulianitis, Dimitrios Ataloglou, A. Dimou, D. Zarpalas, P. Daras

{"title":"Does Deep Super-Resolution Enhance UAV Detection?","authors":"Vasileios Magoulianitis, Dimitrios Ataloglou, A. Dimou, D. Zarpalas, P. Daras","doi":"10.1109/AVSS.2019.8909865","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909865","url":null,"abstract":"The popularity of Unmanned Aerial Vehicles (UAVs) is increasing year by year and reportedly their applications hold great shares in global technology market. Yet, since UAVs can be also used for illegal actions, this raises various security issues that needs to be encountered. Towards this end, UAV detection systems have emerged to detect and further anticipate inimical drones. A very significant factor is the maximum detection range in which the system's senses can “see” an upcoming UAV. For those systems that employ optical cameras for detecting UAVs, the main issue is the accurate drone detection when it fades away into sky. This work proposes the incorporation of Super-Resolution (SR) techniques in the detection pipeline, to increase its recall capabilities. A deep SR model is utilized prior to the UAV detector to enlarge the image by a factor of 2. Both models are trained in an end-to-end manner to fully exploit the joint optimization effects. Extensive experiments demonstrate the validity of the proposed method, where potential gains in the detector's recall performance can reach up to 32.4%.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134135474","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: 38

Fast Tracking-by-Detection of Bus Passengers with Siamese CNNs 基于连体cnn的公交乘客检测快速跟踪

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909843

Claire Labit-Bonis, Jérôme Thomas, F. Lerasle, Francisco Madrigal

引用次数: 0

Gaussian Normalization: Handling Burstiness in Visual Data 高斯归一化:处理视觉数据中的突发性

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909857

Rémi Trichet, N. O’Connor

引用次数: 0

A Camera Recalibration Method for a Top-View Surveillance System based on Relative Camera Pose and Structural Similarity 基于相对摄像机姿态和结构相似度的俯视监控系统摄像机再标定方法

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909870

Jun Minagawa, K. Okahara, Kento Yamazaki, Tsukasa Fukasawa

引用次数: 1

An Exploration on Temperature Term in Training Deep Neural Networks 温度项在深度神经网络训练中的探索

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909875

Zhaofeng Si, H. Qi

引用次数: 0