Current research on skeleton tracking techniques focus on image processing in conjunction with a video camera constrained by bones and joint movement detection limits. The paper proposed 3D skeleton tracking technique using a depth camera known as a Kinect sensor with the ability to approximate human poses to be captured, reconstructed and displayed 3D skeleton in the virtual scene using OPENNI, NITE Primesense and CHAI3D open source libraries. The technique could perform the bone joint movement detections in real time with correct position tracking and display a 3D skeleton in a virtual environment with abilities to control 3D character movements for the future research.

Skeleton Tracking using Kinect Sensor & Displaying in 3D Virtual Scene

Video tracking is one of the fields of recent development in the field of computer vision. A lot of research has been going on in this area and new algorithms are being proposed to detect and track objects in video. This field of study has experienced a sudden growth especially after robotics has started gaining importance. The objective of this paper is to present the different steps involved in tracking objects in a video sequence, namely object detection, object classification and object tracking. We survey the different methods available for detecting, classifying and tracking objects in a detailed manner. The pros and cons of each of the methods are discussed. Object detection methods are frame differencing, optical flow and background subtraction. Then, objects may be classified based on shape, motion, colour and texture. Tracking methods involve point based tracking, kernel based tracking and silhouette based tracking. Keywords— object detection, object classification, object tracking, video processing.

/pdf/a-survey-on-object-detection-classification-and-tracking-4wi5g14bkk.pdf

A Survey on Object Detection, Classification and Tracking Methods

Background subtraction is the important part of moving object detection. The problem of background subtraction is threshold selection strategy. This paper proposed a Fuzzy C-Means (FCM) algorithm to produce an adaptive threshold for background subtraction in moving object detection. To evaluate the performance, FCM were compared against standard Otsu algorithm as threshold selection strategy. Mean Square Error (MSE) and Peak Signal Noise Ratio (PSNR) was used to measure the performance. Based on the experiment, the MSE of FCM is lower than MSE of Otsu and PSNR of FCM is higher than PSNR of Otsu. The result proved that FCM is promising to classify the pixels as foreground or background in moving object detection.

Adaptive threshold for background subtraction in moving object detection using Fuzzy C-Means clustering

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered.

/pdf/recent-advances-in-video-analytics-for-rail-network-2t1lauou.pdf

Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

Advancement in image processing techniques and automation in industrial sector urge its usage in almost all the fields. Fruit classification and grading with its image still remain a challenging task. Fruit classification can be used to perform the sorting and grading process automatically. A traditional method for fruits classification is manual sorting which is time consuming and involves human presence always. Automated sorting process can be used to implement Smart Fresh Park. In this paper, various methods used for fruit classification have experimented. Different fruits considered for classification are five categories of apple, banana, orange and pomegranate. Results were compared by applying the fruit-360 dataset between typical machine learning and deep learning algorithms. To apply machine learning algorithms, basic features of the fruit like the color (RGB Color space), size, height and width were extracted from its image. Traditional machine learning algorithms KNN and SVM were applied over the extracted features. The result shows that using Convolutional Neural Network (CNN) gives a promising result than traditional machine learning algorithms.

Fruit Classification Using Traditional Machine Learning and Deep Learning Approach

This paper attempts to find moving objects by subtracting the background images from static single camera video sequences in security systems. It aims to improve the background subtraction techniques for indoor video surveillance applications. For dynamic video sequences, the object focus and object tracking are very difficult due to changing of various parameters such as camera noise, illumination changes, motion changes, and geometry of background changes. The background subtraction algorithm provides the solution to detect moving objects in the static scene. The recursive and non-recursive algorithms are discussed in an improved way. The novel automatic threshold updating (ATU) algorithm is also developed and tested for various indoor video sequences which gives better efficiency. The statistical and temporal differencing methods are also presented. Finally, our novel approach is compared with the existing methods.

Improved background subtraction techniques for security in video applications

Computer based automated monitoring and detection of human movements is an interesting and important research problem in the video based applications. The segmentation of human body plays a vital role in the analysis of human activities from the indoor video sequences. The threshold value which uses to separate the human body from the background of video frame. The aim of this paper is to develop an approach for segmenting the human body using background subtraction techniques. This proposed algorithm uses Automatic Threshold Update (ATU) and Discrete Wavelet Transform (DWT) approaches for extracting the human body. It is implemented and tested on uniform light and bright light videos.

http://nopr.niscair.res.in/bitstream/123456789/28743/1/JSIR%2073%285%29%20342-345.pdf

Background Subtraction Techniques for Human body segmentation in Indoor video surveillance

Tracking of human body and their pose models from a video data is important in the indoor surveillance applications. The objective of this paper is to develop a human pose modelling in two-dimensional and three dimensional views using wavelet based skin colour segmentation for the monocular videos. The approximation information of the Discrete Wavelet Transform (DWT) is used for segmenting the human body from the video frame and to find the skin features. The human pose modelling is proposed by detecting thirteen features from the upper body as well as in the lower body. Here, twelve human poses are considered for the analysis. In this paper, the hardware system is implemented for monitoring the human poses by sending the SMS message. Also, the efficiencies of 2D modelling and 3D modelling for the pre-defined poses are evaluated.

Skin colour segmentation based 2D and 3D human pose modelling using Discrete Wavelet Transform

Monitoring the behavior and activities of people in Video surveillance has gained more applications in Computer vision. This paper proposes a new approach to model the human body in 2D view for the activity analysis using Thinning algorithm. The first step of this work is Background subtraction which is achieved by the frame differencing algorithm. Thinning algorithm has been used to find the skeleton of the human body. After thinning, the thirteen feature points like terminating points, intersecting points, shoulder, elbow, and knee points have been extracted. Here, this research work attempts to represent the body model in three different ways such as Stick figure model, Patch model and Rectangle body model. The activities of humans have been analyzed with the help of 2D model for the pre-defined poses from the monocular video data. Finally, the time consumption and efficiency of our proposed algorithm have been evaluated.

Development of 2d human body modeling using thinning algorithm

The development of human pose models has become more focused on monitoring and behavioural analysis of people in many video processing applications. The goal of this paper is to develop a human pose modelling using discrete wavelet transform (DWT) for the monocular videos. The human body from the video frame is extracted using wavelet transform. In this work, the low-frequency information of the wavelet features is considered for the analysis. The pose models are developed by detecting 13 features from the human body. For the motion analysis, 12 pre-defined human poses are considered. Also, the wavelet-based Delaunay triangulation modelling (DTM) is proposed for 2D models. In this paper, the hardware system is implemented for monitoring the human poses by sending the SMS message. Finally, the performance of human pose models is computed.

An approach of wavelet transform-based human pose models for indoor video applications

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

G. Sainarayanan

Author Tools

Chat about Author