2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)最新文献

Benefits of Synthetically Pre-trained Depth-Prediction Networks for Indoor/Outdoor Image Classification 综合预训练深度预测网络在室内外图像分类中的应用

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2023-01-01 DOI: 10.1109/WACVW58289.2023.00040

Ke Lin, Irene Cho, Ameya S. Walimbe, Bryan A. Zamora, Alex Rich, Sirius Z. Zhang, Tobias Höllerer

{"title":"Benefits of Synthetically Pre-trained Depth-Prediction Networks for Indoor/Outdoor Image Classification","authors":"Ke Lin, Irene Cho, Ameya S. Walimbe, Bryan A. Zamora, Alex Rich, Sirius Z. Zhang, Tobias Höllerer","doi":"10.1109/WACVW58289.2023.00040","DOIUrl":"https://doi.org/10.1109/WACVW58289.2023.00040","url":null,"abstract":"Ground truth depth information is necessary for many computer vision tasks. Collecting this information is chal-lenging, especially for outdoor scenes. In this work, we propose utilizing single-view depth prediction neural networks pre-trained on synthetic scenes to generate relative depth, which we call pseudo-depth. This approach is a less expen-sive option as the pre-trained neural network obtains ac-curate depth information from synthetic scenes, which does not require any expensive sensor equipment and takes less time. We measure the usefulness of pseudo-depth from pre-trained neural networks by training indoor/outdoor binary classifiers with and without it. We also compare the difference in accuracy between using pseudo-depth and ground truth depth. We experimentally show that adding pseudo-depth to training achieves a 4.4% performance boost over the non-depth baseline model on DIODE, a large stan-dard test dataset, retaining 63.8% of the performance boost achieved from training a classifier on RGB and ground truth depth. It also boosts performance by 1.3% on another dataset, SUN397, for which ground truth depth is not avail-able. Our result shows that it is possible to take information obtained from a model pre-trained on synthetic scenes and successfully apply it beyond the synthetic domain to real-world data.","PeriodicalId":306545,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116758902","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

A Transformer-based Late-Fusion Mechanism for Fine-Grained Object Recognition in Videos 基于变压器的视频细粒度目标识别后期融合机制

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2023-01-01 DOI: 10.1109/WACVW58289.2023.00015

Jannik Koch, Stefan Wolf, Jürgen Beyerer

引用次数: 0

Subjective and Objective Video Quality Assessment of High Dynamic Range Sports Content 高动态范围运动内容的主客观视频质量评价

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2023-01-01 DOI: 10.1109/WACVW58289.2023.00062

Zaixi Shang, Yixu Chen, Yongjun Wu, Hai Wei, S. Sethuraman

{"title":"Subjective and Objective Video Quality Assessment of High Dynamic Range Sports Content","authors":"Zaixi Shang, Yixu Chen, Yongjun Wu, Hai Wei, S. Sethuraman","doi":"10.1109/WACVW58289.2023.00062","DOIUrl":"https://doi.org/10.1109/WACVW58289.2023.00062","url":null,"abstract":"High Dynamic Range (HDR) video streaming has be-come more popular because of the faithful color and bright-ness presentation. However, the live streaming of HDR, especially of sports content, has unique challenges, as it was usually encoded and distributed in real-time without the post-production workflow. A set of unique problems that occurs only in live streaming, e.g. resolution and frame rate crossover, intra-frame pulsing video quality defects, complex relationship between rate-control mode and video quality, are more salient when the videos are streamed in HDR format. These issues are typically ignored by other subjective databases, disregard the fact that they have a sig-nificant impact on the perceived quality of the videos. In this paper, we present a large-scale HDR video quality dataset for sports content that includes the above mentioned important issues in live streaming, and a method of merging multi-ple datasets using anchor videos. We also benchmarked ex-isting video quality metrics on the new dataset, particularly over the novel scopes included in the database, to evaluate the effectiveness and efficiency of the existing models. We found that despite the strong overall performance over the entire database, most of the tested models perform poorly when predicting human preference for various encoding pa-rameters, such as frame rate and adaptive quantization.","PeriodicalId":306545,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114523064","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

Sonar Image Composition for Semantic Segmentation Using Machine Learning 基于机器学习的声纳图像合成语义分割

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2023-01-01 DOI: 10.1109/WACVW58289.2023.00031

William Ard, Corina Barbalata

引用次数: 0

Exploiting Temporal Context for Tiny Object Detection 利用时间背景进行微小目标检测

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2023-01-01 DOI: 10.1109/WACVW58289.2023.00013

Christof W. Corsel, Michel van Lier, L. Kampmeijer, N. Boehrer, E. Bakker

{"title":"Exploiting Temporal Context for Tiny Object Detection","authors":"Christof W. Corsel, Michel van Lier, L. Kampmeijer, N. Boehrer, E. Bakker","doi":"10.1109/WACVW58289.2023.00013","DOIUrl":"https://doi.org/10.1109/WACVW58289.2023.00013","url":null,"abstract":"In surveillance applications, the detection of tiny, low-resolution objects remains a challenging task. Most deep learning object detection methods rely on appearance features extracted from still images and struggle to accurately detect tiny objects. In this paper, we address the problem of tiny object detection for real-time surveillance applications, by exploiting the temporal context available in video sequences recorded from static cameras. We present a spatiotemporal deep learning model based on YOLOv5 that exploits temporal context by processing sequences of frames at once. The model drastically improves the identification of tiny moving objects in the aerial surveillance and person detection domains, without degrading the detection of stationary objects. Additionally, a two-stream architecture that uses frame-difference as explicit motion information was proposed, further improving the detection of moving objects down to $4times 4$ pixels in size. Our approaches outperform previous work on the public WPAFB WAMI dataset, as well as surpassing previous work on an embedded NVIDIA Jetson Nano deployment in both accuracy and inference speed. We conclude that the addition of temporal context to deep learning object detectors is an effective approach to drastically improve the detection of tiny moving objects in static videos.","PeriodicalId":306545,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133556628","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: 4

Attentive Sensing for Long-Range Face Recognition 远程人脸识别的细心感知

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2023-01-01 DOI: 10.1109/WACVW58289.2023.00068

Hélio Perroni Filho, Aleksander Trajcevski, K. Bhargava, Nizwa Javed, J. Elder

引用次数: 0

Knowledge-based Visual Context-Aware Framework for Applications in Robotic Services 基于知识的视觉上下文感知框架在机器人服务中的应用

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2023-01-01 DOI: 10.1109/WACVW58289.2023.00012

Doosoo Chang, Bohyung Han

引用次数: 0

Observation Centric and Central Distance Recovery for Athlete Tracking 观察中心和中心距离恢复运动员跟踪

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2023-01-01 DOI: 10.1109/WACVW58289.2023.00050

Hsiang-Wei Huang, Cheng-Yen Yang, Samartha Ramkumar, Chung-I Huang, Jenq-Neng Hwang, Pyong-Kun Kim, Kyoungoh Lee, Kwang-Ik Kim

{"title":"Observation Centric and Central Distance Recovery for Athlete Tracking","authors":"Hsiang-Wei Huang, Cheng-Yen Yang, Samartha Ramkumar, Chung-I Huang, Jenq-Neng Hwang, Pyong-Kun Kim, Kyoungoh Lee, Kwang-Ik Kim","doi":"10.1109/WACVW58289.2023.00050","DOIUrl":"https://doi.org/10.1109/WACVW58289.2023.00050","url":null,"abstract":"Multi-Object Tracking on humans has improved rapidly with the development of object detection and re-identification algorithms. However, multi-actor tracking over humans with similar appearance and non-linear movement can still be very challenging even for the state-of-the-art tracking algorithm. Current motion-based tracking algorithms often use Kalman Filter to predict the motion of an object, however, its linear movement assumption can cause failure in tracking when the target is not moving linearly. And for multi-player tracking over the sports field, because the players on the same team are usually wearing the same color of jersey, making re-identification even harder both in the short term and long term in the tracking process. In this work, we proposed a motion-based tracking algorithm and three post-processing pipelines for three sports including basketball, football, and volleyball, we successfully handle the tracking of the non-linear movement of players on the sports fields. Experimental results achieved a HOTA of 73.968 on the testing set of ECCV DeeperAction Challenge SportsMOT Dataset and a HOTA of 49.97 on the McGill HPTDataset, showing the effectiveness of the proposed framework and its robustness in different sports including basketball, football, hockey, and volleyball.","PeriodicalId":306545,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127171578","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: 8

Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings Multi-IVE:人脸嵌入中多重软生物特征的隐私增强

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2023-01-01 DOI: 10.1109/WACVW58289.2023.00036

Pietro Melzi, H. O. Shahreza, C. Rathgeb, Rubén Tolosana, R. Vera-Rodríguez, Julian Fierrez, S. Marcel, C. Busch

{"title":"Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings","authors":"Pietro Melzi, H. O. Shahreza, C. Rathgeb, Rubén Tolosana, R. Vera-Rodríguez, Julian Fierrez, S. Marcel, C. Busch","doi":"10.1109/WACVW58289.2023.00036","DOIUrl":"https://doi.org/10.1109/WACVW58289.2023.00036","url":null,"abstract":"This study focuses on the protection of soft-biometric at-tributes related to the demographic information of individ-uals that can be extracted from compact representations of face images, called embeddings. We consider a state-of-the-art technology for soft-biometric privacy enhancement, Incremental Variable Elimination (IVE), and propose Multi-IVE, a new method based on IVE to secure multiple soft-biometric attributes simultaneously. Several aspects of this technology are investigated, proposing different approaches to effectively identify and discard multiple soft-biometric at-tributes contained in face embeddings. In particular, we consider a domain transformation using Principle component Analysis (PCA), and apply IVE in the PCA domain. A complete analysis of the proposed Multi-IVE algorithm is carried out studying the embeddings generated by state-of-the-art face feature extractors, predicting soft-biometric attributes contained within them with multiple machine learning classifiers, and providing a cross-database evaluation. The results obtained show the possibility to simultane-ously secure multiple soft-biometric attributes and support the application of embedding domain transformations be-fore addressing the enhancement of soft-biometric privacy.","PeriodicalId":306545,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130320426","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

An Efficient Approach for Underwater Image Improvement: Deblurring, Dehazing, and Color Correction 一种有效的水下图像改进方法:去模糊、去雾和色彩校正

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) Pub Date : 2023-01-01 DOI: 10.1109/WACVW58289.2023.00026

Alejandro Rico Espinosa, Declan McIntosh, A. Albu

{"title":"An Efficient Approach for Underwater Image Improvement: Deblurring, Dehazing, and Color Correction","authors":"Alejandro Rico Espinosa, Declan McIntosh, A. Albu","doi":"10.1109/WACVW58289.2023.00026","DOIUrl":"https://doi.org/10.1109/WACVW58289.2023.00026","url":null,"abstract":"As remotely operated underwater vehicles (ROV) and static underwater video and image collection platforms become more prevalent, there is a significant need for effective ways to increase the quality of underwater images at faster than real-time speeds. To this end, we present a novel state-of-the-art end-to-end deep learning architecture for underwater image enhancement focused on solving key image degradations related to blur, haze, and color casts and inference efficiency. Our proposed architecture builds from a minimal encoder-decoder structure to address these main underwater image degradations while maintaining efficiency. We use the discrete wavelet transform skip connections and channel attention modules to address haze and color corrections while preserving model efficiency. Our minimal architecture operates at 40 frames per second while scoring a structural similarity index (SSIM) of 0.8703 on the underwater image enhancement benchmark (UIEDB) dataset. These results show our method to be twice as fast as the previous state-of-the-art. We also present a variation of our proposed method with a second parallel deblurring branch for even more significant image improvement, which achieves an improved SSIM of 0.8802 while operating more efficiently than almost all comparable methods. The source code is available at https://github.com/alejorico98/underwater_ddc","PeriodicalId":306545,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130846759","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