Eventmd: High-Speed Moving Object Detection Based on Event-Based Video Frames

2022 ◽  
Author(s):  
SHIXIONG ZHANG ◽  
Wenmin Wang ◽  
Honglei Li ◽  
Shenyong Zhang
Keyword(s):  
Object Detection ◽  
High Speed ◽  
Moving Object Detection ◽  
Moving Object ◽  
Video Frames ◽  
Event Based

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 via TV-L1 optical flow in fall-down videos

2019 ◽  
Vol 8 (3) ◽  
pp. 839-846
Author(s):  
Nur Ayuni Mohamed ◽  
Mohd Asyraf Zulkifley
Keyword(s):  
Object Detection ◽  
Optical Flow ◽  
High Speed ◽  
Ground Truth ◽  
Human Motion ◽  
Performance Measure ◽  
Moving Object Detection ◽  
Moving Object ◽  
Human Motion Analysis ◽  
Surveillance Systems

There is a growing demand for surveillance systems that can detect fall-down events because of the increased number of surveillance cameras being installed in many public indoor and outdoor locations. Fall-down event detection has been vigorously and extensively researched for safety purposes, particularly to monitor elderly peoples, patients, and toddlers. This computer vision detector has become more affordable with the development of high-speed computer networks and low-cost video cameras. This paper proposes moving object detection method based on human motion analysis for human fall-down events. The method comprises of three parts, which are preprocessing part to reduce image noises, motion detection part by using TV-L1 optical flow algorithm, and performance measure part. The last part will analyze the results of the object detection part in term of the bounding boxes, which are compared with the given ground truth. The proposed method is tested on Fall Down Detection (FDD) dataset and compared with Gunnar-Farneback optical flow by measuring intersection over union (IoU) of the output with respect to the ground truth bounding box. The experimental results show that the proposed method achieves an average IoU of 0.92524.

A Moving Object Detection Algorithm Based on ORB under Dynamic Scene

2014 ◽  
Vol 602-605 ◽  
pp. 1638-1641 ◽  
Author(s):  
Wen Hao Luo
Keyword(s):  
Object Detection ◽  
Difference Method ◽  
High Speed ◽  
Detection Algorithm ◽  
Moving Object Detection ◽  
Moving Object ◽  
Transformation Model ◽  
Dynamic Scene ◽  
Frame Difference ◽  
Inter Frame

In this thesis, a moving object detection algorithm under dynamic scene is proposed, which is based on ORB feature. Firstly, we extract feature points and match them by using ORB. We then obtain global motion compensation image by parameters of transformation matrix based on the RANSAC method. Finally, we use the inter-frame difference method to achieve the detection of moving targets. The high speed and accuracy of ORB feature point matching method, as well as the effectiveness of the RANSAC method for removing outliers ensure accurate calculation of parameters of affine transformation model. Combined with inter-frame difference method, foreground objects can be detected entirely. Experiment results show that the algorithm can accurately detect moving objects, and to some extent, it can solve the issue of real-time detection.

Comparative Study on Moving Object Detection Method of Video Surveillance System in China High-Speed Railway Transport Hub

2011 ◽  
Vol 467-469 ◽  
pp. 503-508 ◽  
Author(s):  
Zheng Yu Xie ◽  
Bao Tian Dong ◽  
Ying Chen ◽  
Qian Li
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
High Speed ◽  
Detection Method ◽  
Moving Object Detection ◽  
Moving Object ◽  
Video Surveillance System ◽  
Railway Transport ◽  
High Speed Railway

To detect moving objects from a video sequence is a fundamental and critical task in many computer vision applications. For China high-speed railway transport hub video surveillance system, we need a stable, fast and accurate moving object detection method to promptly find the congestion of passenger flow and other dangerous in hub. Through the comparative study on moving object detection, we find the average/median filtering is the most appropriate method for China high-speed railway transport hub video surveillance.

scholarly journals Moving Object Detection for Event-based Vision using Graph Spectral Clustering

2021 ◽  
Author(s):  
Anindya Mondal ◽  
Shashant R ◽  
Jhony H. Giraldo ◽  
Thierry Bouwmans ◽  
Ananda S. Chowdhury
Keyword(s):  
Object Detection ◽  
Spectral Clustering ◽  
Moving Object Detection ◽  
Moving Object ◽  
Event Based
Sign in / Sign up

Export Citation Format

Share Document