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

Information theory based pruning for CNN compression and its application to image classification and action recognition 基于信息理论的CNN压缩剪枝及其在图像分类和动作识别中的应用

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909826

Hai-Hong Phan, Ngoc-Son Vu

{"title":"Information theory based pruning for CNN compression and its application to image classification and action recognition","authors":"Hai-Hong Phan, Ngoc-Son Vu","doi":"10.1109/AVSS.2019.8909826","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909826","url":null,"abstract":"Convolutional neural networks (CNNs) have become the power method for many computer vision applications, including image classification and action recognition. However, they are almost computationally and memory intensive, thus are challenging to use and to deploy on systems with limited resources, except for a few recent networks which were specifically designed for mobile and embedded vision applications such as MobileNet, NASNet-Mobile. In this paper, we present a novel efficient algorithm to compress CNN models to decrease the computational cost and the run-time memory footprint. We propose a strategy to measure the redundancy of parameters based on their relationship using the covariance and correlation criteria, and then prune the less important ones. Our method directly applies to CNNs, both on convolutional and fully connected layers, and requires no specialized software/hardware accelerators. The proposed method significantly reduces the model sizes (up to 70%) and thus computing costs without performance loss on different CNN models (AlexNet, ResNet, and LeNet) for image classification on different datasets (MNIST, CIFAR10, and ImageNet) as well as for human action recognition (on dataset like the UCF101).","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"79 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":"115488062","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

Structural Low-Rank Tracking 结构低阶跟踪

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909852

S. Javed, A. Mahmood, J. Dias, N. Werghi

{"title":"Structural Low-Rank Tracking","authors":"S. Javed, A. Mahmood, J. Dias, N. Werghi","doi":"10.1109/AVSS.2019.8909852","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909852","url":null,"abstract":"Visual object tracking is an important step for many computer vision applications. The task becomes very challenging when the target undergoes heavy occlusion, background clutters, and sudden illumination variations. Methods that incorporate sparse representation and low-rank assumptions on the target particles have achieved promising results. However, because of the lack of structural constraints, these methods show performance degradation when an object faces the aforementioned challenges. To alleviate these limitations, we propose a new structural low-rank modeling algorithm for robust object tracking. In the proposed algorithm, we enforce local spatial, global spatial and temporal appearance consistency among the particles in the low-rank subspace by constructing three graphs. The Laplacian matrices of these graphs are incorporated into the novel low-rank objective function which is solved using linearized alternating direction method with an adaptive penalty. Our proposed objective function jointly learns the spatial, global, and temporal structure of the target particles in consecutive frames and makes the proposed tracker consistent against many complex tracking scenarios. Results on two challenging benchmark datasets show the superiority of the proposed algorithm as compared to current state-of-the-art methods.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"179 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":"121058070","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

Real-Time Video-Based Person Re-Identification Surveillance with Light-Weight Deep Convolutional Networks 基于轻量深度卷积网络的实时视频人物再识别监控

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909855

Chien-Yao Wang, Ping-Yang Chen, Ming-Chiao Chen, J. Hsieh, H. Liao

{"title":"Real-Time Video-Based Person Re-Identification Surveillance with Light-Weight Deep Convolutional Networks","authors":"Chien-Yao Wang, Ping-Yang Chen, Ming-Chiao Chen, J. Hsieh, H. Liao","doi":"10.1109/AVSS.2019.8909855","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909855","url":null,"abstract":"Today's person re-ID system mostly focuses on accuracy and ignores efficiency. But in most real-world surveillance systems, efficiency is often considered the most important focus of research and development. Therefore, for a person re-ID system, the ability to perform real-time identification is the most important consideration. In this study, we implemented a real-time multiple camera video-based person re-ID system using the NVIDIA Jetson TX2 platform. This system can be used in a field that requires high privacy and immediate monitoring. This system uses YOLOv3-tiny based light-weight strategies and person re-ID technology, thus reducing 46% of computation, cutting down 39.9% of model size, and accelerating 21% of computing speed. The system also effectively upgrades the pedestrian detection accuracy. In addition, the proposed person re-ID example mining and training method improves the model's performance and enhances the robustness of cross-domain data. Our system also supports the pipeline formed by connecting multiple edge computing devices in series. The system can operate at a speed up to 18 fps at 1920×1080 surveillance video stream. The demo of our developed systems can be found at https://sites.google.com/g.ncu.edu.tw/video-based-person-re-id/.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"31 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":"125350437","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

Coarse-to-Fine Object Detection for Ride-Hailing Market Analysis 面向网约车市场分析的粗到精目标检测

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909887

Alvin Prayuda Juniarta Dwiyantoro, K. Muchtar, Faris Rahman, Muhammad Wiryahardiyanto, Reynaldy Hardiyanto

引用次数: 0

Spatio-Temporal Feature Extraction and Distance Metric Learning for Unconstrained Action Recognition 无约束动作识别的时空特征提取和距离度量学习

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909868

Yongsang Yoon, Jongmin Yu, M. Jeon

引用次数: 0

Self-Subtraction Network for End to End Noise Robust Classification 端到端噪声鲁棒分类的自减法网络

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909821

Donghyeon Kim, D. Han, Hanseok Ko

引用次数: 0

Vein Pattern Visualisation and Feature Extraction using Sparse Auto-Encoder for Forensic Purposes 基于稀疏自编码器的法医静脉模式可视化和特征提取

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909860

Soheil Varastehpour, H. Sharifzadeh, I. Ardekani, Xavier Francis

{"title":"Vein Pattern Visualisation and Feature Extraction using Sparse Auto-Encoder for Forensic Purposes","authors":"Soheil Varastehpour, H. Sharifzadeh, I. Ardekani, Xavier Francis","doi":"10.1109/AVSS.2019.8909860","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909860","url":null,"abstract":"Child sexual abuse is a serious global problem that has gained public attention in recent years. Due to the popularity of digital cameras, many perpetrators take images of their sexual activities. Traditionally, it has been difficult to use vein patterns in evidence images for forensic identification, because they were nearly invisible in colour images. State-of-the-art techniques, and computational methods including optical-based vein uncovering or artificial neural networks have recently been introduced to extract vein patterns for identification purposes. However, these methods are still not mature due to limitations such as lack of reliable feature extraction, efficient uncovering algorithms, and matching difficulties. In this paper, we propose two new schemes to overcome some of these limitations by using sparse auto-encoder and adaptive contrast enhancement. Specifically, an adjustment sparse auto-encoder parameters scheme is used for optimising parameters, and then optimised parameters are automatically trained to enhance the robustness of vein visualisation and feature extraction. We also use a pair of synchronised colour and near infrared NIR images to generate the skeletonised vein patterns for verifying the outcome of the proposed method. The proposed algorithm was examined on a database with 100 pairs of colour and NIR images collected from different parts of the body such as forearms, thighs, chests and ankles. The experimental results are encouraging and indicate that the proposed method improves the feature extraction procedure, which can lead to better uncovering results compared with current methods.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"9 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":"131683548","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

Deep Single Image Enhancer 深度单图像增强器

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909891

M. Lin, Jie Yang, O. Yadid-Pecht

{"title":"Deep Single Image Enhancer","authors":"M. Lin, Jie Yang, O. Yadid-Pecht","doi":"10.1109/AVSS.2019.8909891","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909891","url":null,"abstract":"Surveillance cameras can be deployed in various environments where lighting conditions are constantly changing. However, due to the limited dynamic range of current image sensors, the captured images are only low dynamic range images that usually suffer from over-exposure and under-exposure situations where important details are lost. Therefore, it is critical to recover the lost details of such images in order to improve visual experience for observers and performance for possible computer vision processing. In this paper, we propose a reformulated Laplacian pyramid and a convolutional neural network (CNN) model to enhance and recover the lost detail of a degraded image. The reformulated Laplacian first decomposes the image into two sub-images that contain global and local image features, respectively. The global features and local features are processed by the proposed CNN model to manipulate the global luminance terrain and enhance local details. The final image is obtained by reconstructing the CNN generated local and global features. Various experiments have been conducted. The results demonstrate that the proposed model outperforms the state-of-the-art methods.","PeriodicalId":243194,"journal":{"name":"2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"17 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":"115385803","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

CNN-Based Analysis of Crowd Structure using Automatically Annotated Training Data 基于cnn的训练数据自动标注人群结构分析

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909846

M. S. Zitouni, A. Sluzek, H. Bhaskar

{"title":"CNN-Based Analysis of Crowd Structure using Automatically Annotated Training Data","authors":"M. S. Zitouni, A. Sluzek, H. Bhaskar","doi":"10.1109/AVSS.2019.8909846","DOIUrl":"https://doi.org/10.1109/AVSS.2019.8909846","url":null,"abstract":"A CNN-based framework is presented for extracting and classifying from static images of crowd (acquired from surveillance systems) individuals, small groups and large groups. A novel approach to the network training has been investigated. Instead of manually outlined ground-truth data, we use automatic annotations by alternative baseline algorithms (which consider both motion and appearance). The proposed CNN detectors are initially trained over rather limited amounts of data. Nevertheless, the detectors are subsequently updated (fine-tuned) by using new batches of automatically annotated samples. Those test samples are periodically acquired by the baseline algorithms from the future surveillance data. Fine-tuning is performed when noticeable differences appear between results by the CNN-detectors and the results of baseline algorithms (which may indicate changes in visual conditions, scenarios or updates in the baseline algorithms). We preliminarily demonstrate that satisfactory performances of CNN-based detectors can be achieved, even if the baseline algorithms have limited accuracy. Actually, it was noticed that fine-tuned CNN-detectors can be superior to the baseline algorithms used for automatic annotation of training data (even though the baseline algorithms process both static images and video-sequences). Since only static images are used once the detectors are fully trained, the presented solution can simplify complexity of systems automatically evaluating structure and behavior of crowds.","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":"124166202","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

Using Algorithm Selection for Adaptive Vehicle Perception Aboard UAV 基于算法选择的无人机自适应车辆感知

2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2019-09-01 DOI: 10.1109/AVSS.2019.8909862

Christian Hellert, S. Koch, P. Stütz

引用次数: 9