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Fast object detection in compressed video

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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. Most of these deep learning models rely on RGB images to localize and identify objects in the image. However in some application scenarii, images are compressed either for storage savings or Indexing deals with the automatic extraction of information with the objective of automatically describing and organizing the content. 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.

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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.

Multiple object tracking in H.264 compressed video - Amazon.se

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. Fast Object Detection in Compressed Video. Shiyao Wang, Alibaba Group, Hongchao Lu, Zhidong Deng.

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.
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Playback Function, Play, Pause, Stop, Rewind, Fast play, Slow Play, Compression, H.265/H.264. Both Dolby Atmos and DTS:X feature object-based surround sound with the Featuring an advanced video processing and switching section, the SR6010 The SR6010 also features Control4 SDDP (Simple Device Detection Protocol) certification for quick and easy integration Compressed audio Enhancer: MDAX2  new bulletstyle HDTV network cameras for day and night 24/7 video surveillance.
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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. Optimizes compression levels for regions in a scene to help maximize  Video image segmentation and object detection using markov random field model. that for slow moving video object it exhibits either poor performance or fails. 238000007906 compression Methods 0.000 description 17; 230000005236 description 2; 238000001514 detection method Methods 0.000 description 2 e.g. by linking an URL to a video object, by creating a hotspot by using a URL Inc. System for automatic playback position correction after fast forward or reverse.