Multicamera Video Stitching for Multiple Human Tracking

2021 ◽  
Vol 11 (1) ◽  
pp. 17-38
Author(s):  
S. Vasuhi ◽  
A. Samydurai ◽  
Vijayakumar M.
Keyword(s):  
Fuzzy Inference ◽  
Human Tracking ◽  
Foreground Extraction ◽  
Inference System ◽  
Video Stitching ◽  
Novel Approach ◽  
Key Points ◽  
Feature Correspondences ◽  
Centroid Point ◽  
Stitching Techniques

In this paper, a novel approach is proposed to track humans for video surveillance using multiple cameras and video stitching techniques. SIFT key points are extracted from all camera inputs. Using k-d tree algorithm, all the key points are matched and random sample consensus (RANSAC) is used to identify the match correspondence among all the matched points. Homography matrix is calculated using four matched robust feature correspondences, the images are warped with respect to the other images, and the human tracking is performed on the stitched image. To identify the human in the stitched video, background modeling is performed using fuzzy inference system and perform foreground extraction. After foreground extraction, the blobs are constructed around each detected human and centroid point is calculated for each blob. Finally, tracking of multiple humans is done by Kalman filter (KF) with Hungarian algorithm.

An Intuitionistic Fuzzy Based Novel Approach to CPU Scheduler

2020 ◽  
Vol 16 (4) ◽  
pp. 316-328
Author(s):  
Supriya Raheja
Keyword(s):  
Fuzzy Inference ◽  
The Other ◽  
Simulation Environment ◽  
Inference System ◽  
Intuitionistic Fuzzy ◽  
Novel Approach ◽  
Continuous Feedback ◽  
Simulation Results ◽  
Effectiveness And Efficiency ◽  
Unique Capability

Background: The extension of CPU schedulers with fuzzy has been ascertained better because of its unique capability of handling imprecise information. Though, other generalized forms of fuzzy can be used which can further extend the performance of the scheduler. Objectives: This paper introduces a novel approach to design an intuitionistic fuzzy inference system for CPU scheduler. Methods: The proposed inference system is implemented with a priority scheduler. The proposed scheduler has the ability to dynamically handle the impreciseness of both priority and estimated execution time. It also makes the system adaptive based on the continuous feedback. The proposed scheduler is also capable enough to schedule the tasks according to dynamically generated priority. To demonstrate the performance of proposed scheduler, a simulation environment has been implemented and the performance of proposed scheduler is compared with the other three baseline schedulers (conventional priority scheduler, fuzzy based priority scheduler and vague based priority scheduler). Results: Proposed scheduler is also compared with the shortest job first CPU scheduler as it is known to be an optimized solution for the schedulers. Conclusion: Simulation results prove the effectiveness and efficiency of intuitionistic fuzzy based priority scheduler. Moreover, it provides optimised results as its results are comparable to the results of shortest job first.

A Novel Approach of Restoration of Digital Images Degraded by Impulse Noise

2014 ◽  
Vol 4 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Rashmi Kumari ◽  
Anupriya Asthana ◽  
Vikas Kumar
Keyword(s):  
Impulse Noise ◽  
Fuzzy Inference ◽  
Digital Images ◽  
Window Size ◽  
Qualitative And Quantitative Analysis ◽  
Inference System ◽  
Qualitative And Quantitative ◽  
Novel Approach ◽  
Neuro Fuzzy ◽  
Restoration Method

Restoration of digital images degraded by impulse noise is still a challenge for researchers. Various methods proposed in the literature suffer from common drawbacks: such as introduction of artifacts and blurring of the images. A novel idea is proposed in this paper where presence of impulsive pixels are detected by ANFIS (Adaptive Neuro-Fuzzy Inference System) and mean of the median of suitable window size of noisy image is taken for the removal of the detected corrupted pixels. Experimental results show the effectiveness of the proposed restoration method both by qualitative and quantitative analysis.

A New Approach to Realizing the Soft-and Operation in Cloud Model-Based Control

2019 ◽  
Vol 27 (05) ◽  
pp. 755-771
Author(s):  
Jing Wang ◽  
Alessandro Ferrero ◽  
Qi Zhang ◽  
Marco Prioli
Keyword(s):  
Traditional Method ◽  
Fuzzy Inference ◽  
Cloud Model ◽  
Inference System ◽  
Model Based Control ◽  
Model Based ◽  
Novel Approach ◽  
Numeric Simulation ◽  
Cloud Model Theory ◽  
Cloud Control System

Considering fuzziness, randomness, and the association between them, cloud model-based control is a new way to address uncertainty in the inference system. Similar to fuzzy control theory, this method includes an important step of dealing with the logic concept “and”, which is defined as the operation of soft-and between several antecedents and has not been scientifically solved in the current literatures. The traditional method of realizing soft-and is to use multi-dimensional cloud model theory, which lacks a theoretical basis. Based on the fuzzy and random theory, this paper proposes a novel approach using numeric simulation to calculate the soft-and in the cloud control system. In this method, the theory to determine the distribution of the minimum value between two random variables is applied. Compared with the traditional method, the considered approach is more reliable and reasonable, and its result is also in accordance with the standard fuzzy inference system.

Computational Intelligence Techniques for Assessing Anthropometric Indices Changes in Female Athletes

2020 ◽  
Vol 16 (4) ◽  
pp. 288-295 ◽  
Author(s):  
Mahnaz Kazemipoor ◽  
Mehdi Rezaeian ◽  
Maryam Kazemipoor ◽  
Sareena Hamzah ◽  
Shishir Kumar Shandilya
Keyword(s):  
Female Athletes ◽  
Fuzzy Inference ◽  
Predictive Accuracy ◽  
Hybrid Approach ◽  
Anthropometric Indices ◽  
Thigh Circumference ◽  
Inference System ◽  
Natural Medicine ◽  
Novel Approach ◽  
Fuzzy Techniques

Background: Physical characteristics including body size and configuration, are considered as one of the key influences on the optimum performance in athletes. Despite several analyzing methods for modeling the slimming estimation in terms of reduction in anthropometric indices, there are still weaknesses of these models such as being very demanding including time taken for analysis and accuracy. Objective: This research proposes a novel approach for determining the slimming effect of a herbal composition as a natural medicine for weight loss. Methods: To build an effective prediction model, a modern hybrid approach, merging adaptivenetwork- based fuzzy inference system and particle swarm optimization (ANFIS-PSO) was constructed for prediction of changes in anthropometric indices including waist circumference, waist to hip ratio, thigh circumference and mid-upper arm circumference, on female athletes after consumption of caraway extract during ninety days clinical trial. Results: The outcomes showed that caraway extract intake was effective on lowering all anthropometric indices in female athletes after ninety days trial. The results of analysis by ANFIS-PSO was more accurate compared to SPSS. Also, the efficiency of the proposed approach was confirmed using the existing data. Conclusion: It is concluded that a development in predictive accuracy and simplification capability could be attained by hybrid adaptive neuro-fuzzy techniques as modern approaches in detecting changes in body characteristics. These developed techniques could be more useful and valid than other conventional analytical methods for clinical applications.

Sign in / Sign up

Export Citation Format

Share Document