Human Motion Detection Using Background Subtraction Algorithm (original) (raw)

Background Subtraction Algorithm Based Human Motion Detection

Recent research in computer vision has increasingly focused on building systems for observing humans and understanding their look, activities, and behavior providing advanced interfaces for interacting with humans, and creating sensible models of humans for various purposes. In order for any of these systems to function, they require methods for detecting people from a given input image or a video. Visual analysis of human motion is currently one of the most active research topics in computer vision. In which the moving human body detection is the most important part of the human body motion analysis, the purpose is to detect the moving human body from the background image in video sequences, and for the follow-up treatment such as the target classification, the human body tracking and behavior understanding, its effective detection plays a very important role. Human motion analysis concerns the detection, tracking and recognition of people behaviors, from image sequences involving humans.According to the result of moving object detection research on video sequences. This paper presents a new algorithm for detecting moving objects from a static background scene to detect moving object based on background subtraction. We set up a reliable background updating model based on statistical. After that, morphological filtering is initiated to remove the noise and solve the background interruption difficulty. At last, contour projection analysis is combined with the shape analysis to remove the effect of shadow; the moving human bodies are accurately and reliably detected. The experiment results show that the proposed method runs rapidly, exactly and fits for the concurrent detection.

Review of Human Motion Detection based on Background Subtraction Techniques

International Journal of Computer Applications, 2015

For the majority of computer vision applications, the ability to identify and detect objects in motion has become a crucial necessity. Background subtraction, also referred to as foreground detection is an innovation used with image processing and computer vision fields when trying to detect an object in motion within videos from static cameras. This is done by deducting the present image from the image in the background or background module. There has been comprehensive research done in this field as an effort to precisely obtain the region for the use of further processing (e.g. object recognition). This paper provides a review of the human motion detection methods focusing on background subtraction technique.

Background Subtraction Algorithm Based Human Behavior Detection

Consider all the features of subset information in video streaming there is a tremendous processes with real time applications. In this paper we introduce and develop a new video surveillance system. Using this technique we detect human normal and exponential behaviors in realistic format, and also we categories data event generation of human tracking in real time applications. In this technique we apply differencing, threshold segmentation, morphological operations and object tracking. The experimental result show efficient human tracking in video streaming operations.

Comparison of human detection using background subtraction and frame difference

Bulletin of Electrical Engineering and Informatics

Image processing is mostly used for exploring image behaviour. There are several steps in image processing. Image acquisition, pre-processing, feature extraction, and classification are the processes used for the detection of human movement based on high-level feature extraction (HLFE), in which HLFE was used for feature extraction in this paper. This study proposed the use of background subtraction and frame difference. This research was conducted to analyse the difference of background subtraction and frame difference methods based on movement of human. Movement of human detected by using feature extraction were centroid image technique used. Furthermore, support vector machine (SVM) was used for classification.

Efficient Human Motion Detection with Adaptive Background for Vision-Based Security System

International Journal on Advanced Science, Engineering and Information Technology

Motion detection is very important in video surveillance system especially for video compression, human detection, and behaviour analysis. Various approaches have been used for detecting motion in a continuous video stream but for real-time video surveillance system; we need a motion detection that can provide accurate detection even in non-static background regardless of surroundings (outdoor or indoor), object speed and size, robust to camera noisy pixels or sudden change in light intensity. This is very important to ensure that the security of a monitored parameter or area is not compromised. In this paper, we propose a method for human motion detection that employs adaptive background subtraction, camera noise reduction and white pixel count threshold for real-time video streams.

Human Motion Detection and Tracking for Real-Time Security System

IJARCCE

People detection and tracking is one of the important research fields that have gained a lot of attention in the last few years. Although person detection and counting systems are commercially available today, there is a need for further research to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect problematic situations or unusual behavior. Automated video surveillance system addresses real-time observation of people within a busy environment leading to the description of their actions and interactions. It requires detection and tracking of people to ensure security, safety and site management. Object detection is one of the fundamental steps in automated video surveillance. Object detection from the video sequence is mainly performed by background subtraction technique. It is widely used approach for detecting moving objects from static cameras. As the name suggests, background subtraction is the process of separating out the foreground objects from the background in a sequence of video frames. The main aim of the surveillance system here is, to detect and track an object in motion by using single camera. Camera is fixed at the required place background subtraction algorithm is used for segmenting moving object in video. If human entity is detected the tracking lines are formed around human and the object is tracked. The system when realizes the human entry, it is processed in a second and the alert is produced for the security purpose. The main aim is to develop a realtime security system.

Human motion detection and tracking for video surveillance

www-scf.usc.edu

An Automated Video Surveillance system is presented in this paper. The system aims at tracking an object in motion and classifying it as a Human or Non-Human entity, which would help in subsequent human activity analysis. The system employs a novel combination of an Adaptive Background Modeling Algorithm (based on the Gaussian Mixture Model) and a Human Detection for Surveillance (HDS) System. The HDS system incorporates a Histogram of Oriented Gradients based human detector which is well known for its performance in detecting humans in still images. Detailed analysis is carried out on the performance of the system on various test videos.

A Survey on Human Motion Detection and Surveillance

Over years detecting human beings in a video scene of a surveillance system is one of the most active research topics in computer vision. This interest is driven by wide applications in many areas such as virtual reality, smart surveillance and perceptual interface, human gait characterization person counting in a dense crowd, person identification, gender classification, and fall detection for elderly people. Video surveillance system mainly deals with tracking and classification of moving objects. The general processing steps of human motion detection for video surveillance includes modeling of environments, detection of motion, object detection and classification human detection, activity recognition and behavior understanding. The aim of this paper is to review recent developments and analyze future open directions in visual surveillance systems.

HUMAN MOTION DETECTION SYSTEM (VIDEO MOTION DETECTION MODULE

This dissertation is submitted as a partial fulfillment for the award of a Bachelor of Computer Science (Hons) at the Universiti Tunku Abdul Rahman. The work is the result of my own investigations. All sections of the text and results which have been obtained from other workers/sources are fully referenced. I understand that cheating and plagiarism constitute a breach of University regulations and will be dealt with accordingly.