scholarly journals Moving object detection in the HEVC compressed domain for ultra-high-resolution interactive video

2017 ◽  
Author(s):  
Johan De Praeter ◽  
Jan Van de Vyver ◽  
Niels Van Kets ◽  
Glenn Van Wallendael ◽  
Steven Verstockt
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Interactive Video ◽  
Moving Object Detection ◽  
Moving Object ◽  
Compressed Domain

scholarly journals Real-Time Moving Object Detection in High-Resolution Video Sensing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3591 ◽  
Author(s):  
Haidi Zhu ◽  
Haoran Wei ◽  
Baoqing Li ◽  
Xiaobing Yuan ◽  
Nasser Kehtarnavaz
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Real Time ◽  
Moving Object Detection ◽  
Moving Object ◽  
Computationally Efficient ◽  
Real Time Processing ◽  
Time Processing ◽  
Video Frames ◽  
High Resolution Images

This paper addresses real-time moving object detection with high accuracy in high-resolution video frames. A previously developed framework for moving object detection is modified to enable real-time processing of high-resolution images. First, a computationally efficient method is employed, which detects moving regions on a resized image while maintaining moving regions on the original image with mapping coordinates. Second, a light backbone deep neural network in place of a more complex one is utilized. Third, the focal loss function is employed to alleviate the imbalance between positive and negative samples. The results of the extensive experimentations conducted indicate that the modified framework developed in this paper achieves a processing rate of 21 frames per second with 86.15% accuracy on the dataset SimitMovingDataset, which contains high-resolution images of the size 1920 × 1080.

scholarly journals Moving object detection in the H.264/AVC compressed domain

2016 ◽  
Vol 5 ◽  
Author(s):  
Marcus Laumer ◽  
Peter Amon ◽  
Andreas Hutter ◽  
André Kaup
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Spatiotemporal Analysis ◽  
Temporal Analysis ◽  
Detection Algorithm ◽  
Single Step ◽  
Moving Object Detection ◽  
Moving Object ◽  
Compressed Domain ◽  
Video Streams

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. The algorithm is able to perform either a spatiotemporal analysis in a single step or a two-step analysis that starts with a spatial analysis of each frame, followed by a temporal analysis of several subsequent frames. Thereby, in each mode either only (sub-)macroblock types and partition modes or, additionally, quantization parameters are analyzed. The evaluation of these syntax elements enables the algorithm to determine a “weight” for each 4×4 block of pixels that indicates the level of motion within this block. A final segmentation after creating these weights segments each frame to foreground and background and hence indicates the positions and sizes of all moving objects. Our experiments show that the algorithm is able to efficiently detect moving objects in the compressed domain and that it is configurable to process a large number of parallel bit streams in real time.

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