REAL-TIME OBJECT TRACKING IN MOVING CAMERA

2006 ◽  
Vol 03 (01) ◽  
pp. 61-67
Author(s):  
BYOUNG-JU YUN ◽  
JOONG-HOON CHO ◽  
JAE-WOO JEONG
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Region Of Interest ◽  
Block Matching ◽  
Camera Motion ◽  
Moving Camera ◽  
Block Matching Algorithm ◽  
Difference Image ◽  
Human Figure ◽  
The Difference

Moving object tracking plays an important role in applications of object based video conference, video surveillance and so on. The computational complexity is very important in real-time object tracking. We assumed that the background scene is obtained before an object appears in the image and a camera moves after the object is detected. The proposed method can segment an object by using the difference image if there is no camera motion. After camera motion, it can track the object by using the backward BMA (block matching algorithm) with the HFM (human figure model). For real-time tracking, we used the ROI (region of interest) which is the tightest rectangle of the object. The simulation results show that the proposed method efficiently recognizes and tracks the moving camera as well as the fixed camera.

Real-time parking slot availability for Bhavnagar, using statistical block matching approach

2020 ◽  
Vol 17 (6) ◽  
pp. 811-821
Author(s):  
Janak D. Trivedi ◽  
Sarada Devi Mandalapu ◽  
Dhara H. Dave
Keyword(s):  
Real Time ◽  
Region Of Interest ◽  
Mean Value ◽  
Block Matching ◽  
Computational Time ◽  
Maintenance Cost ◽  
Content Type ◽  
Block Matching Algorithm ◽  
The Difference ◽  
Parking Place

Purpose The purpose of this paper is to find a real-time parking location for a four-wheeler. Design/methodology/approach Real-time parking availability using specific infrastructure requires a high cost of installation and maintenance cost, which is not affordable to all urban cities. The authors present statistical block matching algorithm (SBMA) for real-time parking management in small-town cities such as Bhavnagar using an in-built surveillance CCTV system, which is not installed for parking application. In particular, data from a camera situated in a mall was used to detect the parking status of some specific parking places using a region of interest (ROI). The method proposed computes the mean value of the pixels inside the ROI using blocks of different sizes (8 × 10 and 20 × 35), and the values were compared among different frames. When the difference between frames is more significant than a threshold, the process generates “no parking space for that place.” Otherwise, the method yields “parking place available.” Then, this information is used to print a bounding box on the parking places with the color green/red to show the availability of the parking place. Findings The real-time feedback loop (car parking positions) helps the presented model and dynamically refines the parking strategy and parking position to the users. A whole-day experiment/validation is shown in this paper, where the evaluation of the method is performed using pattern recognition metrics for classification: precision, recall and F1 score. Originality/value The authors found real-time parking availability for Himalaya Mall situated in Bhavnagar, Gujarat, for 18th June 2018 video using the SBMA method with accountable computational time for finding parking slots. The limitations of the presented method with future implementation are discussed at the end of this paper.

scholarly journals Moving Object Tracking Algorithm for Complex Shape and Occlusion Handling

2019 ◽  
Vol 9 (1) ◽  
pp. 6299-6304
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Background Subtraction ◽  
Traffic Monitoring ◽  
Block Matching ◽  
Moving Object ◽  
Subtraction Method ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Moving Object Tracking

An object tracking increases loads of enthusiasm for dynamic research in applications such as video surveillance, vehicle navigation, highways, crowded public places, borders, forest and traffic monitoring areas. The system we develop aims to measure and analyze the application of background subtraction method and block matching algorithm to trace object movements through video-based. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection and tracking. This research applies background subtraction method to detect moving object, assisted with block matching algorithm which aims to get good results on objects that have been detected. Performance evaluation is carried out to determine the various parameters. In this paper author design and develop a novel algorithm for moving object tracking in video surveillance also compares and analyse existing algorithms for moving object tracking. Author main aim to design and develop an algorithm for moving object tracking to handle occlusion and complex object shapes.

Injecting Mold Protection Method Based on Machine Vision

2012 ◽  
Vol 590 ◽  
pp. 475-482 ◽  
Author(s):  
Ping Jiang Wang ◽  
Chang Jie Xu ◽  
Ji Hong Chen ◽  
Xiao Qi Tang
Keyword(s):  
Foreign Body ◽  
Near Infrared ◽  
Region Of Interest ◽  
Difference Image ◽  
Background Updating ◽  
Surrounding Environment ◽  
Protection Method ◽  
The Difference ◽  
Background Image ◽  
Speed And Accuracy

Aimed at solving the problem of mold damage caused by a foreign body in the mold before mold clamping, this paper proposes a solution, which applies image processing technology such as background updating and the difference image algorithm to solve it. Not only can it judge whether there is a foreign body in the mold but it can also detect whether the product is perfect by comparing the foreground image with the background image at the appropriate time (before mold clamping or after mold opening) and by calculating the qualified rate of pixel in all ROIs (Region of Interest). To eliminate the influence of vibration and of changes in brightness in the surrounding environment on the detecting results, this paper utilizes near infrared illumination technology and the background updating algorithm. In addition, the ROI is set to improve the detecting speed and accuracy.

HIGH PERFORMANCE, LOW-COMPLEXITY LINE-BASED MOTION ESTIMATION ALGORITHM WITH SMOOTHING AND PREPROCESSING

2009 ◽  
Vol 23 (01) ◽  
pp. 101-114 ◽  
Author(s):  
LI WERN CHEW ◽  
WAI CHONG CHIA ◽  
LI-MINN ANG ◽  
KAH PHOOI SENG
Keyword(s):  
Motion Estimation ◽  
High Performance ◽  
Reference Frames ◽  
Estimation Algorithm ◽  
Low Complexity ◽  
Block Matching ◽  
Absolute Difference ◽  
Current Frame ◽  
Block Matching Algorithm ◽  
The Difference

This paper introduces a smoothing and preprocessing (S+P) technique for a line-based one-bit-transform (1BT) motion estimation scheme. In the proposed algorithm, a smoothing threshold ( Threshold S) is incorporated into the 1BT convolutional kernel. By using the smoothing threshold, scattering noise which is a common problem in most 1BT images can be greatly reduced. After the transformation, the 1BT images for the current and reference frames are divided into a number of macroblocks. The macroblock in the current frame is first compared with the macroblock at the same position in the reference frame. If the Sum of Absolute Difference (SAD) is below a certain preprocessing threshold ( Threshold P), the macroblock in the current frame is considered to have negligible movement and motion search is not performed. Simulation results show that this technique achieves high performance and greatly reduces the number of search operations. By incorporating the S+P technique, the PSNR achieved by the 1BT is approaches the performance of the 8-bit Full Search Block Matching Algorithm (FSBMA), and the difference is as low as 0.08 dB. In addition, this technique outperforms current state-of-the-art 1BT motion estimation techniques. An improvement in PSNR performance by up to 0.6 dB and a reduction in the number of search operations by 60% to 93% is achieved using video conferencing sequences.

scholarly journals Development of a Novel Algorithm for Moving Object Tracking in Video Surveillance

2019 ◽  
Vol 8 (12) ◽  
pp. 3394-3401
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Background Subtraction ◽  
Block Matching ◽  
Moving Object ◽  
Subtraction Method ◽  
Matching Algorithm ◽  
Block Matching Algorithm ◽  
Detection And Tracking ◽  
Moving Object Tracking

Video surveillance is a process of analyzing video sequences. It involves analysis, interpretation of object behaviors, as well as object detection and tracking. Video processing plays an important role in the industry and computer vision such as online monitoring of assembly processes, video surveillance security system, medical treatment, robot navigation and military, etc. Detection and tracking of human objects is one of the important studies in improving the ability of the surveillance system. The aim of this research work is to measure and analyze the application of background subtraction method and block matching algorithm to trace object movements through video-based. This research applies background subtraction method to detect moving object, assisted with block matching algorithm which aims to get good results on objects that have been detected. Performance evaluation is carried out to determine the various parameters. In this paper author design and develop a novel algorithm for moving object tracking in video surveillance also compares and analyse existing algorithms for moving object tracking. Author main aim to design and develop an algorithm for moving object tracking to handle occlusion and complex object shapes.

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