Fast object detection in compressed video. arXiv preprint arXiv:1811.11057, 2018. [10] Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR, abs/1311.2524, 2013. [11] Ross B. Girshick. Fast R-CNN. CoRR, abs/1504.08083, 2015.

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Fast object detection in compressed video

∙ Tsinghua University ∙ 0 ∙ share. Object detection in videos has drawn increasing attention recently since it is more important in real scenarios. Most of the deep learning methods for video analysis use convolutional neural networks designed for image-wise parsing in a Fast Object Detection in Compressed Video Abstract: Object detection in videos has drawn increasing attention since it is more practical in real scenarios. Most of the deep learning methods use CNNs to process each decoded frame in a video stream individually. In this paper, we propose a fast object detection model that incorporates light-weight motion-aided memory network (MMNet), which can be directly used for H.264 compressed video. 2018-11-27 2018-11-27 Fast Object Detection in Compressed Video. Object detection in videos has drawn increasing attention since it is more practical in real scenarios.

2018-11-01 detection in compressed videos are [ 8], [9]. In [ ], separate CNNs are used for temporally linked I-frame (RGB image), and P-frame (motion and residual arrays) are trained all together. In [9], the authors consider three networks: a CNN feature extraction module based on the raw I-image, a re-P-frames using compressed motion and residual vectors, and 2016-11-21 2021-03-19 To our best knowledge, the MMNet is the first work that investigates a deep convolutional detector on compressed videos.

2798, 3D OBJECT DETECTION USING TEMPORAL LIDAR DATA. 1972, 3D Point Cloud 1745, A FAST METHOD FOR SHAPE TEMPLATE GENERATION 1702, A NON-LOCAL MEAN TEMPORAL FILTER FOR VIDEO COMPRESSION .

MJPEG (only supported by object;missing object;fast moving;parking detection;loitering detection;people compression often produces good detection results, but sometimes a heavily compressed image Fast bildkvalité på träning och varierad bildkvalité på test.19. 3.3.3. Varierad det inte finns att tillgå (exempelvis är det sällan möjligt att få helt icke-komprimerad video från ”Federation Object Model” (FOM). En FOM H5A Camera Line features:Next-Generation Video AnalyticsExpanded object and more accurate detection in crowded scenes so you can detect and act faster.

Fast object detection in compressed video

detection in compressed videos are [ 8], [9]. In [ ], separate CNNs are used for temporally linked I-frame (RGB image), and P-frame (motion and residual arrays) are trained all together. In [9], the authors consider three networks: a CNN feature extraction module based on the raw I-image, a re-P-frames using compressed motion and residual vectors, and

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Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregationprovides a simple, fast, accurate, and end-to-end framework for video recognition (e.g., object detection and semantic segmentation in videos). It is worth noting that: Abstract This paper presents a moving object detection algorithm for H.264/AVC video streams that is applied in the compressed domain. The method is able to extract and analyze several syntax elements from any H.264/AVC-compliant bit stream. The number of analyzed syntax elements depends on the mode in which the method operates.

However in some application scenarii, images are compressed either for storage savings or 压缩视频目标检测MMNet：Fast Object Detection in Compressed Video GLee923 2020-09-22 22:35:28 133 收藏 1 分类专栏：计算机视觉文章标签：计算机视觉视频目标检测压缩视频深度学习 FAST OBJECT TRACKING IN COMPRESSED VIDEOS FOR REAL TIME SURVEILLANCE VIDEO ANALYSIS K.Mehmood, M.Mrak, J.Calic and A.Kondoz The University of Surrey, Guildford, GU2 7XH, UK ABSTRACT The outline of Video analysis for object tracking has a strong demand due to the proliferation of surveillance video applications. 1 Introduction Figure 1: An overview of the proposed Fast YOLO framework for object detection in video. For each video frame I t, an image stack consisting of I t and a reference video frame I r e f is passed into a 1 × 1 convolutional layer to compute a motion probability map. Object detection in still images has drawn a lot of attention over past few years, and with the advent of Deep Learning impressive performances have been achieved with numerous industrial applications.
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To our best knowledge, the MMNet is the first work that investigates a deep convolutional detector on compressed videos. Our method is evaluated on the large-scale ImageNet VID dataset, and the results show that it is 3x times faster than single image detector R-FCN and 10x times faster than high-performance detector MANet at a minor accuracy loss.

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