2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)最新文献_第2页

Injecting Sparsity in Anomaly Detection for Efficient Inference 在异常检测中注入稀疏性以实现高效推理

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663843

Bokyeung Lee, Hanseok Ko

{"title":"Injecting Sparsity in Anomaly Detection for Efficient Inference","authors":"Bokyeung Lee, Hanseok Ko","doi":"10.1109/AVSS52988.2021.9663843","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663843","url":null,"abstract":"Anomaly detection in the video is a challenging problem in computer vision tasks. Deep networks recently have been successfully applied and achieved competitive performance in anomaly detection. Modern deep networks employ many modules which extract important features. The anomaly detection approaches just developed network architecture and inserted additional networks to improve performance, however, these methods generally require a tremendous amount of computational load and training parameters. Because of limitations in the real world such as field equipment, mobile system, etc., reducing the number of trainable parameters and model capacity is an important issue in anomaly detection. Moreover, the method, which improves the performance of the anomaly detection algorithm, should be developed without additional trainable parameters. In this paper, we propose a sparsity injecting module which reinforces the feature representation of the existing model and presents the abnormality score function using sparsity. In experimental results, our sparsity injecting module improves the performance of state-of-the-art methods without additional trainable parameters.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"19 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":"125730193","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

TrichTrack: Multi-Object Tracking of Small-Scale Trichogramma Wasps 三目跟踪:小尺度赤眼蜂的多目标跟踪

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663814

Vishal Pani, M. Bernet, Vincent Calcagno, L. V. Oudenhove, F. Brémond

引用次数: 2

ARPD: Anchor-free Rotation-aware People Detection using Topview Fisheye Camera ARPD:使用Topview鱼眼相机的无锚旋转感知人物检测

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663768

Quan Minh Nguyen, Bang Le Van, Can Nguyen, Anh Le, Viet Dung Nguyen

{"title":"ARPD: Anchor-free Rotation-aware People Detection using Topview Fisheye Camera","authors":"Quan Minh Nguyen, Bang Le Van, Can Nguyen, Anh Le, Viet Dung Nguyen","doi":"10.1109/AVSS52988.2021.9663768","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663768","url":null,"abstract":"People detection in top-view, fish-eye images is challenging as people in fish-eye images often appear in arbitrary directions and are distorted differently. Due to this unique radial geometry, axis-aligned people detectors often work poorly on fish-eye frames. Recent works account for this variability by modifying existing anchor-based detectors or relying on complex pre/post-processing. Anchor-based methods spread a set of pre-defined bounding boxes on the input image, most of which are invalid. In addition to being inefficient, this approach could lead to a significant imbalance between the positive and negative anchor boxes. In this work, we propose ARPD, a single-stage anchor-free fully convolutional network to detect arbitrarily rotated people in fish-eye images. Our network uses keypoint estimation to find the center point of each object and regress the object’s other properties directly. To capture the various orientation of people in fish-eye cameras, in addition to the center and size, ARPD also predicts the angle of each bounding box. We also propose a periodic loss function that accounts for angle periodicity and relieves the difficulty of learning small-angle oscillations. Experimental results show that our method competes favorably with state-of-the-art algorithms while running significantly faster.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"363 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":"115967043","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

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663810

Snehashis Majhi, Srijan Das, F. Brémond

{"title":"DAM: Dissimilarity Attention Module for Weakly-supervised Video Anomaly Detection","authors":"Snehashis Majhi, Srijan Das, F. Brémond","doi":"10.1109/AVSS52988.2021.9663810","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663810","url":null,"abstract":"Video anomaly detection under weak supervision is complicated due to the difficulties in identifying the anomaly and normal instances during training, hence, resulting in non-optimal margin of separation. In this paper, we propose a framework consisting of Dissimilarity Attention Module (DAM) to discriminate the anomaly instances from normal ones both at feature level and score level. In order to decide instances to be normal or anomaly, DAM takes local spatio-temporal (i.e. clips within a video) dissimilarities into account rather than the global temporal context of a video. This allows the framework to detect anomalies in real-time (i.e. online) scenarios without the need of extra window buffer time. Further more, we adopt two-variants of DAM for learning the dissimilarities between successive video clips. The proposed framework along with DAM is validated on two large scale anomaly detection datasets i.e. UCF-Crime and ShanghaiTech, outperforming the online state-of-the-art approaches by 1.5% and 3.4% respectively. The source code and models will be available at https://github.com/snehashismajhi/DAM-Anomaly-Detection","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"153 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":"116733548","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: 9

Geometry-Based Person Re-Identification in Fisheye Stereo 基于几何的鱼眼立体人物再识别

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663745

Joshua Bone, Mertcan Cokbas, M. Tezcan, J. Konrad, P. Ishwar

引用次数: 3

Action Recognition with Fusion of Multiple Graph Convolutional Networks 基于多图卷积网络融合的动作识别

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663765

Camille Maurice, F. Lerasle

引用次数: 0

A Real-time Super-Resolution for Surveillance Thermal Cameras using optimized pipeline on Embedded Edge Device 基于嵌入式边缘设备优化流水线的实时超分辨率监控热像仪

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663831

Prayushi Mathur, A. Singh, Syed Azeemuddin, Jayram Adoni, Prasad Adireddy

引用次数: 4

An Efficient And Robust Framework For Collaborative Monocular Visual Slam 一种高效且稳健的协同单目视觉Slam框架

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663736

Dipanjan Das, Soumyadip Maity, B. Dhara

{"title":"An Efficient And Robust Framework For Collaborative Monocular Visual Slam","authors":"Dipanjan Das, Soumyadip Maity, B. Dhara","doi":"10.1109/AVSS52988.2021.9663736","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663736","url":null,"abstract":"Visual SLAM (VSLAM) has shown remarkable performance in robot navigation and its practical applicability can be enriched by building a multi-robot collaboration framework called Visual collaborative SLAM (CoSLAM). CoSLAM extends the usage of SLAM for navigating in larger areas for certain applications like inspection etc. using multiple vehicles which not only saves time but also power. Visual CoSLAM framework suffers from problems like i) Robot can start from anywhere in the scene using their own VSLAM which save both time and power ii) making the framework independent of the choice of SLAM for greater applicability of different SLAMs, iii) avoiding collision with other robots by a robust merging of two noisy maps, when the visual overlap is detected. Very few works are available in the literature which addresses the above problems in a single framework in a practical sense. In this paper, we present a framework for CoSLAM using monocular cameras addressing all the above problems. Unlike existing systems which work only on ORB SLAM, our framework is truly independent of SLAMs. We propose a deep learning based algorithm to find out the visually overlapped scene required for merging two or more 3D maps. Our Map Merging is robust in presence of outliers as we compute similarity transforms using both structural information as well as camera-camera relationships and choose one based on a statistical inference. Experimental results show that our framework is robust and works well for any individual SLAM where we demonstrate our result on ORB and EdgeSLAM which are prototypical extremes methods for map merging in a CoSLAM framework.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"21 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":"122645400","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

The Dataset and Baseline Models to Detect Human Postural States Robustly against Irregular Postures 基于数据集和基线模型的人体姿态状态鲁棒检测

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663782

K. Bae, Kimin Yun, Jungchan Cho, Yuseok Bae

引用次数: 1

Multi-Pedestrian Tracking with Clusters 基于集群的多行人跟踪

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663829

Daniel Stadler, J. Beyerer

{"title":"Multi-Pedestrian Tracking with Clusters","authors":"Daniel Stadler, J. Beyerer","doi":"10.1109/AVSS52988.2021.9663829","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663829","url":null,"abstract":"One of the biggest challenges in multi-pedestrian tracking arises in crowds, where missing detections can lead to wrong track-detection assignments, especially under heavy occlusion. In order to identify such situations, we cluster tracks and detections based on their overlaps and introduce different cluster states depending on the number of detections and tracks in a cluster. On the basis of this strategy, we make the following contributions. First, we propose a cluster-aware non-maximum suppression (CA-NMS) that leverages temporal information from tracks applying an increased IoU threshold in clusters with severe occlusion to reduce the number of missed detections, while at the same time limiting the number of duplicate detections. Second, for clusters with very high overlaps where detections are missing even with the CA-NMS, we utilize past track information to correct wrong assignments when missed targets are re-detected after occlusion. Furthermore, we propose a new tracking pipeline that combines the paradigms of tracking-by-detection and regression-based tracking to improve the association performance in crowded scenes. Putting all together, our tracker achieves competitive results w.r.t. the state-of-the-art on three multi-pedestrian tracking benchmarks. Our framework is analyzed with extensive ablative experiments and the impact of the proposed tracking components on the performance is evaluated.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"135 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":"116648325","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: 12