Proceedings of the ACM Multimedia Asia最新文献_第5页

Multiple Fisheye Camera Tracking via Real-Time Feature Clustering 基于实时特征聚类的多鱼眼相机跟踪

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366581

Chon-Hou Sio, Hong-Han Shuai, Wen-Huang Cheng

{"title":"Multiple Fisheye Camera Tracking via Real-Time Feature Clustering","authors":"Chon-Hou Sio, Hong-Han Shuai, Wen-Huang Cheng","doi":"10.1145/3338533.3366581","DOIUrl":"https://doi.org/10.1145/3338533.3366581","url":null,"abstract":"Recently, Multi-Target Multi-Camera Tracking (MTMC) makes a breakthrough due to the release of DukeMTMC and show the feasibility of related applications. However, most of the existing MTMC methods focus on the batch methods which attempt to find the global optimal solution from the entire image sequence and thus are not suitable for the real-time applications, e.g., customer tracking in unmanned stores. In this paper, we propose a low-cost online tracking algorithm, namely, Deep Multi-Fisheye-Camera Tracking (DeepMFCT) to identify the customers and locate the corresponding positions from multiple overlapping fisheye cameras. Based on any single camera tracking algorithm (e.g., Deep SORT), our proposed algorithm establishes the correlation between different single camera tracks. Owing to the lack of well-annotated multiple overlapping fisheye cameras dataset, the main challenge of this issue is to efficiently overcome the domain gap problem between normal cameras and fisheye cameras based on existed deep learning based model. To address this challenge, we integrate a single camera tracking algorithm with cross camera clustering including location information that achieves great performance on the unmanned store dataset and Hall dataset. Experimental results show that the proposed algorithm improves the baselines by at least 7% in terms of MOTA on the Hall dataset.","PeriodicalId":273086,"journal":{"name":"Proceedings of the ACM Multimedia Asia","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126118802","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: 7

Multimedia Information Retrieval 多媒体信息检索

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3372212

Yangyang Guo

引用次数: 0

Deep Distillation Metric Learning 深度蒸馏度量学习

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366560

Jiaxu Han, Tianyu Zhao, Changqing Zhang

引用次数: 4

A Performance-Aware Selection Strategy for Cloud-based Video Services with Micro-Service Architecture 基于微服务架构的云视频服务性能感知选择策略

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366609

Zhengjun Xu, Haitao Zhang, Han Huang

{"title":"A Performance-Aware Selection Strategy for Cloud-based Video Services with Micro-Service Architecture","authors":"Zhengjun Xu, Haitao Zhang, Han Huang","doi":"10.1145/3338533.3366609","DOIUrl":"https://doi.org/10.1145/3338533.3366609","url":null,"abstract":"The cloud micro-service architecture provides loosely coupling services and efficient virtual resources, which becomes a promising solution for large-scale video services. It is difficult to efficiently select the optimal services under micro-service architecture, because the large number of micro-services leads to an exponential increase in the number of service selection candidate solutions. In addition, the time sensitivity of video services increases the complexity of service selection, and the video data can affects the service selection results. However, the current video service selection strategies are insufficient under micro-service architecture, because they do not take into account the resource fluctuation of the service instances and the features of the video service comprehensively. In this paper, we focus on the video service selection strategy under micro-service architecture. Firstly, we propose a QoS Prediction (QP) method using explicit factor analysis and linear regression. The QP can accurately predict the QoS values based on the features of video data and service instances. Secondly, we propose a Performance-Aware Video Service Selection (PVSS) method. We prune the candidate services to reduce computational complexity and then efficiently select the optimal solution based on Fruit Fly Optimization (FFO) algorithm. Finally, we conduct extensive experiments to evaluate our strategy, and the results demonstrate the effectiveness of our strategy.","PeriodicalId":273086,"journal":{"name":"Proceedings of the ACM Multimedia Asia","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126988022","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

Selective Attention Network for Image Dehazing and Deraining 图像去雾和去雾的选择性注意网络

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366688

Xiao Liang, Runde Li, Jinhui Tang

引用次数: 8

Video Summarization based on Sparse Subspace Clustering with Automatically Estimated Number of Clusters 基于自动估计簇数的稀疏子空间聚类视频摘要

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366593

Pengyi Hao, Edwin Manhando, Taotao Ye, Cong Bai

{"title":"Video Summarization based on Sparse Subspace Clustering with Automatically Estimated Number of Clusters","authors":"Pengyi Hao, Edwin Manhando, Taotao Ye, Cong Bai","doi":"10.1145/3338533.3366593","DOIUrl":"https://doi.org/10.1145/3338533.3366593","url":null,"abstract":"Advancements in technology resulted in a sharp growth in the number of digital cameras at people's disposal all across the world. Consequently, the huge storage space consumed by the videos from these devices on video repositories make the job of video processing and analysis to be time-consuming. Furthermore, this also slows down the video browsing and retrieval. Video summarization plays a very crucial role in solving these issues. Despite the number of video summarization approaches proposed up to the present time, the goal is to take a long video and generate a video summary in form of a short video skim without losing the meaning or the message transmitted by the original lengthy video. This is done by selecting the important frames called key-frames. The approach proposed by this work performs automatic summarization of digital videos based on detected objects' deep features. To this end, we apply sparse subspace clustering with an automatically estimated number of clusters to the objects' deep features. The summary generated from our scheme will store the meta-data for each short video inferred from the clustering results. In this paper, we also suggest a new video dataset for video summarization. We evaluate the performance of our work using the TVSum dataset and our video summarization dataset.","PeriodicalId":273086,"journal":{"name":"Proceedings of the ACM Multimedia Asia","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132442556","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

Active Perception Network for Salient Object Detection 显著目标检测的主动感知网络

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366580

Junhang Wei, Shuhui Wang, Liang Li, Qingming Huang

{"title":"Active Perception Network for Salient Object Detection","authors":"Junhang Wei, Shuhui Wang, Liang Li, Qingming Huang","doi":"10.1145/3338533.3366580","DOIUrl":"https://doi.org/10.1145/3338533.3366580","url":null,"abstract":"To get better saliency maps for salient object detection, recent methods fuse features from different levels of convolutional neural networks and have achieved remarkable progress. However, the differences between different feature levels bring difficulties to the fusion process, thus it may lead to unsatisfactory saliency predictions. To address this issue, we propose Active Perception Network (APN) to enhance inter-feature consistency for salient object detection. First, Mutual Projection Module (MPM) is developed to fuse different features, which uses high-level features as guided information to extract complementary components from low-level features, and can suppress background noises and improve semantic consistency. Self Projection Module (SPM) is designed to further refine the fused features, which can be considered as the extended version of residual connection. Features that pass through SPM can produce more accurate saliency maps. Finally, we propose Head Projection Module (HPM) to aggregate global information, which brings strong semantic consistency to the whole network. Comprehensive experiments on five benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art approaches on different evaluation metrics.","PeriodicalId":273086,"journal":{"name":"Proceedings of the ACM Multimedia Asia","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134536705","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

Robust Visual Tracking via Statistical Positive Sample Generation and Gradient Aware Learning 基于统计正样本生成和梯度感知学习的鲁棒视觉跟踪

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366556

Lijian Lin, Haosheng Chen, Yanjie Liang, Y. Yan, Hanzi Wang

{"title":"Robust Visual Tracking via Statistical Positive Sample Generation and Gradient Aware Learning","authors":"Lijian Lin, Haosheng Chen, Yanjie Liang, Y. Yan, Hanzi Wang","doi":"10.1145/3338533.3366556","DOIUrl":"https://doi.org/10.1145/3338533.3366556","url":null,"abstract":"In recent years, Convolutional Neural Network (CNN) based trackers have achieved state-of-the-art performance on multiple benchmark datasets. Most of these trackers train a binary classifier to distinguish the target from its background. However, they suffer from two limitations. Firstly, these trackers cannot effectively handle significant appearance variations due to the limited number of positive samples. Secondly, there exists a significant imbalance of gradient contributions between easy and hard samples, where the easy samples usually dominate the computation of gradient. In this paper, we propose a robust tracking method via Statistical Positive sample generation and Gradient Aware learning (SPGA) to address the above two limitations. To enrich the diversity of positive samples, we present an effective and efficient statistical positive sample generation algorithm to generate positive samples in the feature space. Furthermore, to handle the issue of imbalance between easy and hard samples, we propose a gradient sensitive loss to harmonize the gradient contributions between easy and hard samples. Extensive experiments on three challenging benchmark datasets including OTB50, OTB100 and VOT2016 demonstrate that the proposed SPGA performs favorably against several state-of-the-art trackers.","PeriodicalId":273086,"journal":{"name":"Proceedings of the ACM Multimedia Asia","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130307879","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

Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning 基于层次强化学习的弱监督视频摘要

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3338533.3366583

Yiyan Chen, Li Tao, Xueting Wang, T. Yamasaki

{"title":"Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning","authors":"Yiyan Chen, Li Tao, Xueting Wang, T. Yamasaki","doi":"10.1145/3338533.3366583","DOIUrl":"https://doi.org/10.1145/3338533.3366583","url":null,"abstract":"Conventional video summarization approaches based on reinforcement learning have the problem that the reward can only be received after the whole summary is generated. Such kind of reward is sparse and it makes reinforcement learning hard to converge. Another problem is that labelling each shot is tedious and costly, which usually prohibits the construction of large-scale datasets. To solve these problems, we propose a weakly supervised hierarchical reinforcement learning framework, which decomposes the whole task into several subtasks to enhance the summarization quality. This framework consists of a manager network and a worker network. For each subtask, the manager is trained to set a subgoal only by a task-level binary label, which requires much fewer labels than conventional approaches. With the guide of the subgoal, the worker predicts the importance scores for video shots in the subtask by policy gradient according to both global reward and innovative defined sub-rewards to overcome the sparse problem. Experiments on two benchmark datasets show that our proposal has achieved the best performance, even better than supervised approaches.","PeriodicalId":273086,"journal":{"name":"Proceedings of the ACM Multimedia Asia","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123933017","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: 39

Session details: Poster Session 会议详情:海报会议

Proceedings of the ACM Multimedia Asia Pub Date : 2019-12-15 DOI: 10.1145/3379191

Ting Gan

引用次数: 0