Real-time moving object detection using a vehicle-mounted monocular rear-view fisheye camera

2016 ◽  
Author(s):  
Min Su Kim ◽  
Ji-Hye Seo ◽  
Nam Kyu Kwon ◽  
Ju-man Song ◽  
PooGyeon Park
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Moving Object Detection ◽  
Moving Object ◽  
Fisheye Camera

Joint Exploitation of Features and Optical Flow for Real-Time Moving Object Detection on Drones

2019 ◽  
pp. 100-116
Author(s):  
Hazal Lezki ◽  
I. Ahu Ozturk ◽  
M. Akif Akpinar ◽  
M. Kerim Yucel ◽  
K. Berker Logoglu ◽  
...  
Keyword(s):  
Object Detection ◽  
Optical Flow ◽  
Real Time ◽  
Moving Object Detection ◽  
Moving Object

FPGA-based real-time moving object detection for walking robots

2010 ◽  
Author(s):  
Andreas Laika ◽  
Johny Paul ◽  
Christopher Claus ◽  
Walter Stechele ◽  
Adam El Sayed Auf ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Moving Object Detection ◽  
Moving Object ◽  
Walking Robots

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.

Three-Pronged Compensation and Hysteresis Thresholding for Moving Object Detection in Real-Time Video Surveillance

2017 ◽  
Vol 64 (6) ◽  
pp. 4945-4955 ◽  
Author(s):  
Chia-Hung Yeh ◽  
Chih-Yang Lin ◽  
Kahlil Muchtar ◽  
Hsiang-Erh Lai ◽  
Ming-Ting Sun
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Video Surveillance ◽  
Moving Object Detection ◽  
Moving Object ◽  
Hysteresis Thresholding

scholarly journals Real Time Moving Object Detection

IJARCCE ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 81-86
Author(s):  
Prof. Rakhi J. Bharadwaj ◽  
Rohan M. Saggam ◽  
Rushikesh .
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Moving Object Detection ◽  
Moving Object
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