2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)最新文献_第4页

Image-Based Real-Time Fire Detection using Deep Learning with Data Augmentation for Vision-Based Surveillance Applications 基于图像的实时火灾探测，使用深度学习和数据增强，用于基于视觉的监视应用

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909899

Li-Wei Kang, I-Shan Wang, Ke-Lin Chou, Shih-Yu Chen, Chuan-Yu Chang

引用次数: 19

Improving ResNet-based Feature Extractor for Face Recognition via Re-ranking and Approximate Nearest Neighbor 基于重新排序和近似最近邻的改进resnet人脸识别特征提取器

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909884

Sheng-Hsing Hsiao, J. Jang

引用次数: 0

Improved Part-aligned Deep Features Learning for Person Re-Identification 改进的部分对齐深度特征学习用于人物再识别

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909867

Sheng-Luen Chung, Yuchen Xue, S. Chien, Ruei-Shan Chan

{"title":"Improved Part-aligned Deep Features Learning for Person Re-Identification","authors":"Sheng-Luen Chung, Yuchen Xue, S. Chien, Ruei-Shan Chan","doi":"10.1109/AVSS.2019.8909867","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909867","url":null,"abstract":"Person Re-IDentification (Re-ID) is to recognize a person who has been seen before by different cameras from possibly scenes. Re-ID poses as one of the most difficult computer vision problems owing to the enormous amount of identities involved in a large-scale image pool, with much similar appearance constrained by low resolution image, in a possibly occluded scene, etc. Global features geared for general object recognition and face recognition are far less adequate to re-identify a same person across cameras. As such, more discriminating features are needed to identify people. In particular, part-based feature extraction methods that extract by learning local fine- grained features of different human body parts from detected persons have been proved effective for person Re-ID. To further improve the part-aligned spatial feature approach, this paper proposes an improved part-aligned feature (IPAF) deep learning framework to better characterize a person's complete information with the following threes highlights: part alignment, finer part segmentation, and better learning network backbone. Our proposed solution has been trained and tested on the two most comprehensive Re-ID datasets with comparable performance of reported state-of-the-art solutions: for the dataset of Market1501 (DukeMTMC-reID), our proposed solution both achieves competitive results with mAP of 85.96% (84.70%) and CMC 1 of 94.30% (89.84%), respectively.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"66 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":"116987049","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: 1

Continuous Learning without Forgetting for Person Re-Identification 不断学习，不忘，重新认识自己

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909828

Nehemia Sugianto, D. Tjondronegoro, G. Sorwar, Prithwi Raj Chakraborty, E. Yuwono

引用次数: 9

Social MIL: Interaction-Aware for Crowd Anomaly Detection 社会MIL:群体异常检测的交互感知

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909882

Shuheng Lin, Hua Yang, Xianchao Tang, Tianqi Shi, Lin Chen

{"title":"Social MIL: Interaction-Aware for Crowd Anomaly Detection","authors":"Shuheng Lin, Hua Yang, Xianchao Tang, Tianqi Shi, Lin Chen","doi":"10.1109/AVSS.2019.8909882","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909882","url":null,"abstract":"Crowd anomaly detection under surveillance scene is a quite challenging task, which often companies with not rare objects, unexpected bursts in activity and complex dynamic patterns. In this paper, we propose a social multiple-instance learning(MIL) framework with a dual-branch network by considering dynamic interaction among groups, individuals and environment to obtain attentive spatial-temporal feature representation. First, MIL is employed to overcome the challenge of rare training abnormal samples and video-based labels. The social force map is utilized for modeling behavior interaction to supply the prior knowledge. In addition, we introduce the self-attention module, which represents a more discriminative spatial-temporal feature based on C3D network through implementing weight redistribution inside the feature. The results of the experiments conducted on UCF-Crime dataset show that the proposed dual-branch social multiple-instance learning (MIL) anomaly detection framework with the dual-branch network outperforms than existing approaches and obtains the state-of-the-art performance.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"148 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":"121514117","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: 20

Pose Guided Dynamic Image Network for Human Action Recognition in Person Centric Videos 以人为中心视频中人体动作识别的姿态引导动态图像网络

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909835

S. Chaudhary, Akshay Dudhane, Prashant W. Patil, S. Murala

{"title":"Pose Guided Dynamic Image Network for Human Action Recognition in Person Centric Videos","authors":"S. Chaudhary, Akshay Dudhane, Prashant W. Patil, S. Murala","doi":"10.1109/AVSS.2019.8909835","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909835","url":null,"abstract":"The most emerging concerns in computer vision are size of data to process and privacy preserving of the end user. Camera sensors are all around us these days, recording and analysing our day-to-day activities. In this scenario the privacy perseverance becomes a question of concern especially in case of devices working on the basis of human action recognition (HAR). Another important concern in computer vision is the size of data. The surveillance requires continues transfer of huge amount of data through the network. The processing time required to transfer the video to central server and analyses the video directly depends on the resolution of the video. The research in computer vision is exploring the possibility of working on different aspects of videos such as using only pose information or representing whole video using a single frame for the purpose of HAR. Here, an attempt is made to explore the concept of pose estimation and video representation using dynamic image to solve the dual purpose of privacy preserving and decreasing the load on network for transfer of videos over the network for analysis. In this paper, a new Pose Guided Dynamic Image (PDI) network is proposed for HAR which is capable of providing a summarized single frame for the person's activity in any given video. Unlike dynamic image network, this approach considers only the person's motion and discards the background motion. Therefore, PDI provides more specific information required for HAR as compared to the dynamic image. Also, by summarizing the video, the identity of the person remains masked. The proposed method is able to provide better result on both of the benchmark datasets used namely JHMDB and UCF-sports for the experimentation.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"15 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":"122199926","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

Domain is of the Essence: Data Deployment for City-Scale Multi-Camera Vehicle Re-Identification 域是本质:城市规模多摄像头车辆再识别的数据部署

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909858

Mark Schutera, Frank M. Hafner, Hendrik Vogt, Jochen Abhau, M. Reischl

引用次数: 2

Graph-to-Graph Energy Minimization for Video Object Segmentation 基于图对图能量最小化的视频对象分割

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909894

Yuezun Li, Longyin Wen, Ming-Ching Chang, Siwei Lyu

引用次数: 3

Drone Detection in Long-Range Surveillance Videos 远程监控视频中的无人机探测

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909830

Mrunalini Nalamati, Ankit Kapoor, M. Saqib, N. Sharma, M. Blumenstein

{"title":"Drone Detection in Long-Range Surveillance Videos","authors":"Mrunalini Nalamati, Ankit Kapoor, M. Saqib, N. Sharma, M. Blumenstein","doi":"10.1109/AVSS.2019.8909830","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909830","url":null,"abstract":"The usage of small drones/UAVs has significantly increased recently. Consequently, there is a rising potential of small drones being misused for illegal activities such as terrorism, smuggling of drugs, etc. posing high-security risks. Hence, tracking and surveillance of drones are essential to prevent security breaches. The similarity in the appearance of small drone and birds in complex background makes it challenging to detect drones in surveillance videos. This paper addresses the challenge of detecting small drones in surveillance videos using popular and advanced deep learning-based object detection methods. Different CNN-based architectures such as ResNet-101 and Inception with Faster-RCNN, as well as Single Shot Detector (SSD) model was used for experiments. Due to sparse data available for experiments, pre-trained models were used while training the CNNs using transfer learning. Best results were obtained from experiments using Faster-RCNN with the base architecture of ResNet-101. Experimental analysis on different CNN architectures is presented in the paper, along with the visual analysis of the test dataset.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"74 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":"132907346","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: 49

Improving Person Re-Identification by Combining Siamese Convolutional Neural Network and Re-Ranking Process 结合暹罗卷积神经网络和重新排序过程改进人物再识别

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909902

Nabila Mansouri, Sourour Ammar, Yousri Kessentini

引用次数: 5