#13 best model for Video Object Detection on ImageNet VID (MAP metric)

The feature extraction and feature integration parameters are optimized in an end-to-end manner. The proposed video object detection network is evaluated on the large-scale ImageNet VID benchmark and achieves 77.2% mAP, which is on-par with the state-of-the-art accuracy, at …

Thinking of a video stream, different types of information can be considered semantically important. Since we can assume that the most relevant one is linked to the presence of moving foreground objects, their number, their shape, and their appearance can Home Browse by Title Periodicals EURASIP Journal on Advances in Signal Processing Vol. 2008 Multiple moving object detection for fast video content description in compressed domain complex for automatic object tracking in ultra-high resolution interactive panoramic video. Therefore, this paper proposes a fast object detection method in the compressed domain for High Efficiency Video Coding. Evaluation shows promising results for optimal object sizes. I. INTRODUCTION Advances in digital video capturing allow cameras to cap- #13 best model for Video Object Detection on ImageNet VID (MAP metric) 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. Most of these deep learning models rely on RGB images to localize and identify objects in the image Fast compressed domain motion detection in H.264 video streams for video surveillance applications Krzysztof Szczerba, Søren Forchhammer Technical University of Denmark DTU Fotonik Ørsteds Plads b.343 DK-2800 Kgs. Lyngby krsz@fotonik.dtu.dk, sofo@fotonik.dtu.dk Jesper Støttrup-Andersen, Peder Tanderup Eybye Milestone Systems A/S Banemarksvej 50G Video analysis for object tracking has a strong demand due to the proliferation of surveillance video applications.

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- "Fast Object Detection in Compressed Video" But they usually ignore the fact that a video is generally stored and transmitted in a compressed data format. 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. 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 Abstract: This paper discusses a novel fast approach for moving object detection in H.264/AVC compressed domain for video surveillance applications. The proposed algorithm initially segments out edges from regions with motion at macroblock level by utilizing the gradient of quantization parameter over 2D-image space. Request PDF | On Oct 1, 2019, Sami Jaballah and others published Fast Object Detection in H264/AVC and HEVC Compressed Domains for Video Surveillance | Find, read and cite all the research you Indexing deals with the automatic extraction of information with the objective of automatically describing and organizing the content.

Video object detection. Current Video Object Detection (VOD) research is benchmarked by the VID dataset introduced by ILSVRC (Russakovsky et al., 2015a) in year 2017. Exiting video object detection algorithms can be divided into two streams. One is box-level method, the other is feature-level method. We will introduce them in detail as follows.

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Viewing trajectory of a moving object (Motion Shot Video) [41] Face Detection [109] The subjects passing across this product very fast appear crooked. [241] AVC/H.264 format is adopted to compress video data, and the Dolby Digital or 

I grundlösning så finns det en videorecorder för upp till 12 kameror samt alla tillbehör, master camera https://adisupport.de4.quickconnect.to/ user: adi password: DTTQAW Video Compression, Main Stream: H.265+/H.265/H.264+/H.264 A laser sensor can detect objects in all lighting conditions and  the fast response and delivery times you expect. Solutions for your detecting contamination with foreign objects, especially plastic objects.

Object detection in videos has drawn increasing attention since it is more practical in real scenarios. [] The MMNet has two major advantages: 1) It significantly accelerates the procedure of feature extraction for compressed videos. Fast Object Detection in Compressed Video. ICCV 2019 • Shiyao Wang • Hongchao Lu • Zhidong Deng. 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 … 2015-04-11 2018-07-20 Fast object detection in compressed video.
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The filter significantly reduces the noisy motion vectors that do not represent a real object movement . The filter analyses the temporal coherence of block motion vectors to determine if they are likely to represent true motion in 2014-02-01 · Segmenting Foreground objects from a video sequence is a key processing and critical step in video analysis such as object detection and tracking. Several Foreground detection techniques and edge detectors have been developed till now but the problem is that it is very difficult to obtain an optimal foreground due to the interference from the factors like weather, light, shadow and clutter.

2018-11-27 · To our best knowledge, the MMNet is the first work that explores a convolutional detector on a compressed video and a motion-based memory in order to achieve significant speedup. Our model is evaluated on the large-scale ImageNet VID dataset, and the results show that it is about 3x times faster than single image detector R-FCN and 10x times faster than high performance detectors like FGFA and MANet. 2018-11-27 · 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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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.

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. 2019-09-11 2007-08-22 The feature extraction and feature integration parameters are optimized in an end-to-end manner. The proposed video object detection network is evaluated on the large-scale ImageNet VID benchmark and achieves 77.2% mAP, which is on-par with the state-of-the-art accuracy, at … A Fast Object Detecting-Tracking Method in Compressed Domain 3 disappears from the camera view. However, they require an offline training stage and therefore cannot be applied to unknown objects. 2.1 Detection In recently years, Many methods have been developed for moving object de-tection in H.264/AVC bitstream domain. 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. Multiple Moving Object Detection for Fast Video Content Description in Compressed Domain Multiple Moving Object Detection for Fast Video Content Description in Compressed Domain Manerba, Francesca; Benois-Pineau, Jenny; Leonardi, Riccardo; Mansencal, Boris 2007-08-22 00:00:00 Indexing deals with the automatic extraction of information with the objective of automatically describing and improved object detection based on the motion-vector infor-mation presented in compressed videos.