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

Single-Stage UAV Detection and Classification with YOLOV5: Mosaic Data Augmentation and PANet 基于YOLOV5的单级无人机检测与分类:拼接数据增强和PANet

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663841

Fardad Dadboud, Vaibhav Patel, Varun Mehta, M. Bolic, I. Mantegh

引用次数: 22

Person Localisation under Fragmented Occlusion* 碎片遮挡下的人定位*

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663791

R. Pflugfelder, Jonas Auer

引用次数: 0

DSA-PR: Discrete Soft Biometric Attribute-Based Person Retrieval in Surveillance Videos DSA-PR:基于离散软生物特征属性的监控视频人物检索

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663775

Hiren Galiyawala, M. Raval, Dhyey Savaliya

引用次数: 1

Track Boosting and Synthetic Data Aided Drone Detection 航迹增强和合成数据辅助无人机探测

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663759

F. C. Akyon, Ogulcan Eryuksel, Kamil Anil Ozfuttu, S. Altinuc

引用次数: 8

Bayesian Personalized-Wardrobe Model (BP-WM) for Long-Term Person Re-Identification 长期人物再识别的贝叶斯个性化衣橱模型(BP-WM)

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663830

K. Lee, Nishant Sankaran, D. Mohan, Kenny Davila, Dennis Fedorishin, S. Setlur, V. Govindaraju

{"title":"Bayesian Personalized-Wardrobe Model (BP-WM) for Long-Term Person Re-Identification","authors":"K. Lee, Nishant Sankaran, D. Mohan, Kenny Davila, Dennis Fedorishin, S. Setlur, V. Govindaraju","doi":"10.1109/AVSS52988.2021.9663830","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663830","url":null,"abstract":"Long-term surveillance applications often involve having to re-identify individuals over several days. The task is made even more challenging due to changes in appearance features such as clothing over a longitudinal time-span of days or longer. In this paper, we propose a novel approach called Bayesian Personalized-Wardrobe Model (BPWM) for long-term person re-identification (re-ID) by employing a Bayesian Personalized Ranking (BPR) for clothing features extracted from video sequences. In contrast to previous long-term person re-ID works, we exploit the fact that people typically choose their attire based on their personal preferences and that knowing a person’s chosen wardrobe can be used as a soft-biometric to distinguish identities in the long-term. We evaluate the performance of our proposed BP-WM on the extended Indoor Long-term Re-identification Wardrobe (ILRW) dataset. Experimental results show that our method achieves state-of-the-art performance and that BP-WM can be used as a reliable soft-biometric for person re-identification.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"12 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":"121310809","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

Far-Sighted BiSeNet V2 for Real-time Semantic Segmentation 面向实时语义分割的远视距BiSeNet V2

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663738

Te-Wei Chen, Yen-Ting Huang, W. Liao

{"title":"Far-Sighted BiSeNet V2 for Real-time Semantic Segmentation","authors":"Te-Wei Chen, Yen-Ting Huang, W. Liao","doi":"10.1109/AVSS52988.2021.9663738","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663738","url":null,"abstract":"Real-time semantic segmentation is one of the most investigated areas in the field of computer vision. In this paper, we focus on improving the performance of BiSeNet V2 by modifying its architecture. BiSeNet V2 is a two-branch segmentation model designed to extract semantic information from high-level feature maps and detailed information from low-level feature maps. The proposed enhancement remains lightweight and real-time with two main modifications: enlarging the contextual information and breaking the constraint caused by the fixed size of convolutional kernels. Specifically, additional modules known as dilated strip pooling (DSP) and dilated mixed pooling (DMP) are appended to the original BiSeNet V2 model to form the far-sighted BiSeNet V2. The proposed dilated strip pooling block and dilated mixed pooling module are adapted from modules proposed in SPNet, with extra branches composed of dilated convolutions to provide larger receptive fields. The proposed far-sighted BiSeNet V2 improves the accuracy to 76.0% from 73.4% with an FPS of 94 on Nvidia 1080Ti. Moreover, the proposed dilated mixed pooling block achieves the same performance as that of the model with two mixed pooling modules using only 2/3 of the number of parameters.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"18 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":"122207330","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

FlagDetSeg: Multi-Nation Flag Detection and Segmentation in the Wild FlagDetSeg:野外多国国旗检测和分割

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663833

Shou-Fang Wu, Ming-Ching Chang, Siwei Lyu, Cheng-Shih Wong, Ashok Pandey, Po-Chi Su

{"title":"FlagDetSeg: Multi-Nation Flag Detection and Segmentation in the Wild","authors":"Shou-Fang Wu, Ming-Ching Chang, Siwei Lyu, Cheng-Shih Wong, Ashok Pandey, Po-Chi Su","doi":"10.1109/AVSS52988.2021.9663833","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663833","url":null,"abstract":"We present a simple and effective flag detection approach for multi-nation flag instance segmentation in-the-wild based on data augmentation and Mask-RCNN PointRend. To the best of our knowledge, this is the first multi-nation flag detection work incorporating recent deep object detection with code and dataset that will be released for public use. Flag images with binary segmentation are collected from public domain including the Open Image V6 and annotated for up to 225 countries. Additional flag images are generated from template flag images with cropping, warping, masking, and color adaption to hallucinate realistic-looking flag images for training and testing. Data augmentation is performed by fusing and transforming the segmented flags on top of natural image backgrounds to synthesize new images. To cope with the large variability of flags with the lack of authentic annotated flags, we combine the trained binary Mask-RCNN segmentation weights with the new multi-nation classifier for fine-tuning. For evaluation, the proposed model is compared with other popular detectors and instance segmentation methods including YOLACT++. Results show the efficacy of the proposed approach.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"23 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":"123810821","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: 2

A comprehensive maritime benchmark dataset for detection, tracking and threat recognition 用于检测、跟踪和威胁识别的综合海事基准数据集

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663739

J. L. Patino, Tom Cane, J. Ferryman

{"title":"A comprehensive maritime benchmark dataset for detection, tracking and threat recognition","authors":"J. L. Patino, Tom Cane, J. Ferryman","doi":"10.1109/AVSS52988.2021.9663739","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663739","url":null,"abstract":"This paper describes a new multimodal maritime dataset recorded using a multispectral suite of sensors, including AIS, GPS, radar, and visible and thermal cameras. The visible and thermal cameras are mounted on the vessel itself and surveillance is performed around the vessel in order to protect it from piracy at sea. The dataset corresponds to a series of acted scenarios which simulate attacks to the vessel by small, fast-moving boats (‘skiffs’). The scenarios are inspired by real piracy incidents at sea and present a range of technical challenges to the different stages in an automated surveillance system: object detection, object tracking, and event recognition (in this case, threats towards the vessel). The dataset can thus be employed for training and testing at several stages of a threat detection and classification system. We also present in this paper baseline results that can be used for benchmarking algorithms performing such tasks. This new dataset fills a lack of publicly available datasets for the development and testing of maritime surveillance applications.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"25 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":"124948673","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

Learning Sequential Visual Appearance Transformation for Online Multi-Object Tracking 学习在线多目标跟踪的顺序视觉外观变换

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663809

Itziar Sagastiberri, Noud van de Gevel, Jorge García, O. Otaegui

{"title":"Learning Sequential Visual Appearance Transformation for Online Multi-Object Tracking","authors":"Itziar Sagastiberri, Noud van de Gevel, Jorge García, O. Otaegui","doi":"10.1109/AVSS52988.2021.9663809","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663809","url":null,"abstract":"Recent online multi-object tracking approaches combine single object trackers and affinity networks with the aim of capturing object motions and associating objects by using their appearance, respectively. Those affinity networks often build on complex feature representations (re-ID embeddings) or sophisticated scoring functions, whose objective is to match current detections with previous tracklets, known as short-term appearance information. However, drastic appearance changes during the object trajectory acquired by omnidirectional cameras causes a degradation of the performance since affinity networks ignore the variation of the long-term appearance information. In this paper, we deal with the appearance changes in a coherent way by proposing a novel affinity model which is able to predict the new visual appearance of an object by considering the long-term appearance information. Our affinity model includes a convolutional LSTM encoder-decoder architecture to learn the space-time appearance transformation metric between consecutive re-ID feature representations along the object trajectory. Experimental results show that it achieves promising performance on several multi-object tracking datasets containing omnidirectional cameras.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"11 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":"121785657","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

Moving-Object-Aware Anomaly Detection in Surveillance Videos 监控视频中的运动对象感知异常检测

2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2021-11-16 DOI: 10.1109/AVSS52988.2021.9663742

Chun-Lung Yang, Tsung-Hsuan Wu, S. Lai

{"title":"Moving-Object-Aware Anomaly Detection in Surveillance Videos","authors":"Chun-Lung Yang, Tsung-Hsuan Wu, S. Lai","doi":"10.1109/AVSS52988.2021.9663742","DOIUrl":"https://doi.org/10.1109/AVSS52988.2021.9663742","url":null,"abstract":"Video anomaly detection plays a crucial role in automatically detecting abnormal actions or events from surveillance video, which can help to protect public safety. Deep learning techniques have been extensively employed and achieved excellent anomaly detection results recently. However, previous image-reconstruction-based models did not fully exploit foreground object regions for the video anomaly detection. Some recent works applied pre-trained object detectors to provide local context in the video surveillance scenario for anomaly detection. Nevertheless, these methods require prior knowledge of object types for the anomaly which is somewhat contradictory to the problem setting of unsupervised anomaly detection. In this paper, we propose a novel framework based on learning the moving-object feature prediction based on a convolutional autoencoder architecture. We train our anomaly detector to be aware of moving-object regions in a scene without using an object detector or requiring prior knowledge of specific object classes for the anomaly. The appearance and motion features in moving objects regions provide comprehensive information of moving foreground objects for unsupervised learning of video anomaly detector. Besides, the proposed latent representation learning scheme encourages the convolutional autoencoder model to learn a more convergent latent representation for normal training data, while anomalous data exhibits quite different representations. We also propose a novel anomaly scoring method based on the feature prediction errors of moving foreground object regions and the latent representation regularity. Our experimental results demonstrate that the proposed approach achieves competitive results compared with SOTA methods on three public datasets for video anomaly detection.","PeriodicalId":246327,"journal":{"name":"2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"89 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":"122776406","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: 2