scholarly journals Real-Time Detection and Recognition of Multiple Moving Objects for Aerial Surveillance

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1373 ◽  
Author(s):  
Wahyu Rahmaniar ◽  
Wen-June Wang ◽  
Hsiang-Chieh Chen
Keyword(s):  
Real Time ◽  
High Frequency ◽  
Moving Objects ◽  
Region Of Interest ◽  
Computation Time ◽  
Important Application ◽  
Feature Points ◽  
Aerial Surveillance ◽  
Precision Rate ◽  
Detection And Recognition

Detection of moving objects by unmanned aerial vehicles (UAVs) is an important application in the aerial transportation system. However, there are many problems to be handled such as high-frequency jitter from UAVs, small size objects, low-quality images, computation time reduction, and detection correctness. This paper considers the problem of the detection and recognition of moving objects in a sequence of images captured from a UAV. A new and efficient technique is proposed to achieve the above objective in real time and in real environment. First, the feature points between two successive frames are found for estimating the camera movement to stabilize sequence of images. Then, region of interest (ROI) of the objects are detected as the moving object candidate (foreground). Furthermore, static and dynamic objects are classified based on the most motion vectors that occur in the foreground and background. Based on the experiment results, the proposed method achieves a precision rate of 94% and the computation time of 47.08 frames per second (fps). In comparison to other methods, the performance of the proposed method surpasses those of existing methods.

Real-time bi-directional people counting using an RGB-D camera

Sensor Review ◽  
2021 ◽  
Vol 41 (4) ◽  
pp. 341-349
Author(s):  
Wahyu Rahmaniar ◽  
W.J. Wang ◽  
Chi-Wei Ethan Chiu ◽  
Noorkholis Luthfil Luthfil Hakim
Keyword(s):  
Real Time ◽  
Region Of Interest ◽  
Computation Time ◽  
Depth Image ◽  
Fast Computation ◽  
Content Type ◽  
People Counting ◽  
Rgb Images ◽  
Precision Rate ◽  
Rgb Image

Purpose The purpose of this paper is to propose a new framework and improve a bi-directional people counting technique using an RGB-D camera to obtain accurate results with fast computation time. Therefore, it can be used in real-time applications. Design/methodology/approach First, image calibration is proposed to obtain the ratio and shift values between the depth and the RGB image. In the depth image, a person is detected as foreground by removing the background. Then, the region of interest (ROI) of the detected people is registered based on their location and mapped to an RGB image. Registered people are tracked in RGB images based on the channel and spatial reliability. Finally, people were counted when they crossed the line of interest (LOI) and their displacement distance was more than 2 m. Findings It was found that the proposed people counting method achieves high accuracy with fast computation time to be used in PCs and embedded systems. The precision rate is 99% with a computation time of 35 frames per second (fps) using a PC and 18 fps using the NVIDIA Jetson TX2. Practical implications The precision rate is 99% with a computation time of 35 frames per second (fps) using a PC and 18 fps using the NVIDIA Jetson TX2. Originality/value The proposed method can count the number of people entering and exiting a room at the same time. If the previous systems were limited to only one to two people in a frame, this system can count many people in a frame. In addition, this system can handle some problems in people counting, such as people who are blocked by others, people moving in another direction suddenly, and people who are standing still.

Camera Calibration and 3-D Measurement with an Active Stereo Vision System for Handling Moving Objects

2003 ◽  
Vol 15 (3) ◽  
pp. 304-313 ◽  
Author(s):  
Atsushi Yamashita ◽  
◽  
Toru Kaneko ◽  
Shinya Matsushita ◽  
Kenjiro T. Miura ◽  
...  
Keyword(s):  
Real Time ◽  
Camera Calibration ◽  
Stereo Vision ◽  
Moving Objects ◽  
Vision System ◽  
Computation Time ◽  
Stereo Camera ◽  
Stereo Vision System ◽  
Active Stereo ◽  
Camera Parameters

In this paper, we propose a fast, easy camera calibration and 3-D measurement method with an active stereo vision system for handling moving objects whose geometric models are known. We use stereo cameras that change direction independently to follow moving objects. To gain extrinsic camera parameters in real time, a baseline stereo camera (parallel stereo camera) model and projective transformation of stereo images are used by considering epipolar constraints. To make use of 3-D measurement results for a moving object, the manipulator hand approaches the object. When the manipulator hand and object are near enough to be situated in a single image, very accurate camera calibration is executed to calculate the manipulator size in the image. Our calibration is simple and practical because it does not need to calibrate all camera parameters. The computation time for real-time calibration is not large because we need only search for one parameter in real time by deciding the relationship between all parameters in advance. Our method does not need complicated image processing or matrix calculation. Experimental results show that the accuracy of 3-D reconstruction of a cubic box whose edge is 60 mm long is within 1.8 mm when the distance between the camera and the box is 500 mm. Total computation time for object tracking, camera calibration, and manipulation control is within 0.5 seconds.

scholarly journals Automatic Traffic Sign Detection Practices: A Review

2020 ◽  
Vol 6 (12) ◽  
pp. 117-124
Author(s):  
Tania Joseph
Keyword(s):  
Real Time ◽  
Region Of Interest ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Safe Navigation ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition plays an important part in today’s technology driven world. The purpose of traffic signs is to help drivers as well as pedestrians for safe navigation. The two major phases involved in traffic sign detection and recognition are : identifying the region of interest and proceeding to detect any and all signs that might be present, and further, classifying the detected signs into their respective classes. This paper attempts to review all the existing methods/practices for the detection of signs(real-time).

An improved method for swing measurement based on monocular vision to the payload of overhead crane

2020 ◽  
pp. 014233122092131
Author(s):  
Jinling Huang ◽  
Weimin Xu ◽  
Weiwei Zhao ◽  
Hesong Yuan
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Mean Shift ◽  
Region Of Interest ◽  
Image Plane ◽  
Overhead Crane ◽  
Diagram Method ◽  
Mean Shift Algorithm ◽  
Swing Angle ◽  
Motion Speed

In order to solve the problem that the blurred image of a moving object decreases accuracy in the process of detecting the payload swing angle of an overhead crane based on vision, and the tracking failure caused by the drastic change of grey targets, a robust real-time detection method of the load swing angle of a bridge crane is proposed. This method uses a spherical marker attached to the load, which is insensitive to rotation and tilt when it is detected. First, it uses the mean shift algorithm combined with Kalman filter to track the moving objects in the image plane continuously, and then integrates the method of minimum area circle to detect the spherical marker image in the region of interest accurately and quickly. Finally, combined with the geometric method, the real-time swing angle is calculated. In addition, the angle diagram method is used to increase the speed of calculating the swing angle. The experimental results show that the method is effective for detecting the load target swing angle of different trolley motion speed.

scholarly journals Design of an Efficient Real-Time Algorithm Using Reduced Feature Dimension for Recognition of Speed Limit Signs

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Hanmin Cho ◽  
Seungwha Han ◽  
Sun-Young Hwang
Keyword(s):  
Real Time ◽  
Region Of Interest ◽  
Computation Time ◽  
Time Algorithm ◽  
Speed Limit ◽  
Moving Vehicle ◽  
Linear Discriminant ◽  
Hit Rate ◽  
Dct Coefficients ◽  
Feature Dimension

We propose a real-time algorithm for recognition of speed limit signs from a moving vehicle. Linear Discriminant Analysis (LDA) required for classification is performed by using Discrete Cosine Transform (DCT) coefficients. To reduce feature dimension in LDA, DCT coefficients are selected by a devised discriminant function derived from information obtained by training. Binarization and thinning are performed on a Region of Interest (ROI) obtained by preprocessing a detected ROI prior to DCT for further reduction of computation time in DCT. This process is performed on a sequence of image frames to increase the hit rate of recognition. Experimental results show that arithmetic operations are reduced by about 60%, while hit rates reach about 100% compared to previous works.

scholarly journals Automatic Traffic Sign Detection Practices: A Review

2020 ◽  
Vol 6 (12) ◽  
pp. 117-124
Author(s):  
Tania Joseph
Keyword(s):  
Real Time ◽  
Region Of Interest ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Safe Navigation ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition plays an important part in today’s technology driven world. The purpose of traffic signs is to help drivers as well as pedestrians for safe navigation. The two major phases involved in traffic sign detection and recognition are : identifying the region of interest and proceeding to detect any and all signs that might be present, and further, classifying the detected signs into their respective classes. This paper attempts to review all the existing methods/practices for the detection of signs(real-time).

scholarly journals Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

2018 ◽  
Vol 14 (03) ◽  
pp. 34 ◽  
Author(s):  
Xianyan Kuang ◽  
Wenbin Fu ◽  
Liu Yang
Keyword(s):  
Real Time ◽  
Random Forests ◽  
Road Traffic ◽  
Contrast Ratio ◽  
Region Of Interest ◽  
Traffic Sign ◽  
Data Set ◽  
Traffic Signs ◽  
Real Time Detection ◽  
Detection And Recognition

Real-time detection and recognition of road traffic signs plays an important role in advanced driving assistance system. Typically, the region of interest (ROI) method is effective in feature extraction but inefficient because it is sensitive to illumination changes. In this paper, we propose a maximally stable extremal regions (MSER) method with image enhancement to greatly improve ROI. Firstly, we employ gray world algorithm to process original images. And then potential areas of traffic signs are obtained through increasing the image contrast ratio and extracting the image-enhanced MSER. According to the characteristic variable and the geometry moment invariants, the geometric characteristics of traffic signs are extracted to obtain the ROIs. Finally, HSV-HOG-LBP feature is constructed and the random forests algorithm is used to identify the traffic signs. The experimental results show that our proposed method show strong robustness on illumination condition and rotation scale, and achieves a good performance by experiments with actual images and German traffic sign detection benchmark (GTSDB) data set.

Vehicle Detection and Counting in Traffic Video Based on OpenCV

2013 ◽  
Vol 361-363 ◽  
pp. 2232-2235
Author(s):  
Wen Jun Wang ◽  
Meng Gao
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Moving Objects ◽  
Social Economy ◽  
Space Vehicle ◽  
Region Of Interest ◽  
Moving Vehicle ◽  
Real Time Traffic ◽  
Detection And Counting ◽  
Traffic Video

With the development of modern social economy, the number of vehicles in China is growing rapidly, so how to get real-time traffic parameters has a very important significance in using the limited road space, vehicle video detection method based on image processing develop rapidly. With the improvement of image processing technology and microprocessor performance, makes video-based traffic parameter detection using universal. This paper deals with the real-time traffic video, gets each frame, uses Gaussian filter denoising, marks the region of interest (ROI), apply background subtraction algorithm based on average method, get the binarization foreground image, set threshold to eliminate the moving objects whose area is too small, check the boundary of ROI to judge the moving vehicle and counting, get the results as parameters of the intelligent transportation.

Real-Time Moving Objects Tracking for Distributed Smart Video Surveillances

2016 ◽  
Vol 11 (4) ◽  
pp. 324
Author(s):  
Nor Nadirah Abdul Aziz ◽  
Yasir Mohd Mustafah ◽  
Amelia Wong Azman ◽  
Amir Akramin Shafie ◽  
Muhammad Izad Yusoff ◽  
...  
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Objects Tracking
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