Movement Detection and Tracking Using Video Frames

Automated Video Surveillance deals with real-time observation of people and objects within a busy environment leading to a description of their actions and interactions. This paper deals with an advanced image processing method for the motion detection and tracking. An intelligent surveillance system must be able to detect moving objects irrespective of noise present in the surroundings and track the movements. The system employs a novel method of background subtraction and updating the background for foreground extraction and blob labeling for tracking the feature. Detailed analysis on the proposed system will be carried out on real time using Matlab software.

2014

Video surveillance systems have known significant growth because of the increased insecurity in these recent years. In order to reduce threats such as assaults, many cameras have invaded the public squares. The manual monitoring of these screens is tedious because of the large amount of information. So it is very interesting to automate this process from image processing systems able to extract the useful information from video sequences and interpret it. One of the most important tasks is the motion detection and estimation. This article aims to provide the status of art of the different techniques of motion detection estimation and segmentation based on movement. Many studies have been conducted on the subject and the literature is very abundant in this province, we are not trying to list all the existing methods. The idea is to give an overview of the most commonly used methods and to distinguish different types and approaches.

site.iugaza.edu.ps

The rapid development in the field of digital image processing made motion detection and tracking an attractive research topic. Until recent years, real-time video applications were inapplicable due to the expense computational time. An intelligent method to analyze the motion in a stream video line using the methods of background subtraction, temporal differencing, and optical flow, methods are proposed. The new method solves the computational time problem by using a reliable technique that is called Fast Pixels Selection. A low cost tracking system is proposed. This tracking system consist of camera, PC, motor and data acquisition card. This system is designed to detect and track any moving target automatically.

2017

Moving object recognition and detection is very crucial for video surveillance. In this paper, we present a comparative analysis between the various motion detection algorithms like background subtraction, Kalman filter, Mean Shift and Optical flow. The accumulative optical flow method is employed in order to obtain and retain a stable background image and cope with changes in environmental conditions. The performance of optical flow in terms of tracking and detection is much improved and accurate as compared to rest of the other algorithms.From the comparison it has been noted that the optical flow algorithm have outperformed the kalman and the mean shift algorithm .

International Journal of Trend in Scientific Research and Development

Motion detection is the process of detecting moving objects in background images. Motion detection plays a fundamental role in any object tracking or vide surveillance algorithm. The reliability with which potential foreground objects in movement can be identified, directly impacts on the efficiency and performance level achievable by subsequent processing stages of tracking or object recognition. The syst automatically performs a task and gives alert to security in an area. This paper represents review on Motion detection is an essential for many video applications such as video surveillance, military reconnaissance, and robotics. Most of these applications demand low power consumption, compact and lightweight design, and high speed computation platform for processing image data in real time.

arXiv preprint arXiv:1109.6840, 2011

This article describes a comprehensive system for surveillance and monitoring applications. The development of an efficient real time video motion detection system is motivated by their potential for deployment in the areas where security is the main concern. The paper presents a platform for real time video motion detection and subsequent generation of an alarm condition as set by the parameters of the control system. The prototype consists of a mobile platform mounted with RF camera which provides continuous feedback of the environment. The received visual information is then analyzed by user for appropriate control action, thus enabling the user to operate the system from a remote location. The system is also equipped with the ability to process the image of an object and generate control signals which are automatically transmitted to the mobile platform to track the object.

2008 SICE Annual Conference, 2008

The aim of this study is to build a prototype of a multi-camera tracking system for a security system that enables us to track several human motions at one time. In this paper two innovative methods: foot step detection method and particle filtering method. Generally, to recognize a single human motion is easier than to link several human motions. This is because several human motions move different directions, while a single human motion moves one direction in a certain period. Therefore, we need a system that is reliable to track of several human motions in every frame. In this paper we propose a new method for human tracking systems using foot step direction with prediction formula to forecast the direction of human movement. Furthermore, an enhanced method based on particle filtering enables us to detect and track a human movement. This process can enable the system to predict which direction the object will move and also can give the signal to other cameras to aware about that the object will appear. The detail outcome and expected result are discussed in this paper..

Digital video is being used widely in a variety of applications such as surveillance and security. Big amount of video in surveillance and security requires systems capable to process video automatically to detect events and track moving objects to alleviate the load on humans and enable preventive actions when events are detected . our paper focuses to develop an intelligent visual surveillance system to replace the traditional passive video surveillance that is proving ineffective as the number of cameras exceeds the capability of human operators to monitor them, and it is able to track objects within a maximum solid angle speed which is measured at about 0.3 to 0.2 radian per second, further it also depends on the complexity of the system and the processor speed as well.

Nowadays, video surveillance is indispensable in security-sensitive areas. Hence, a significant amount of work has been done in this field. This paper proposes a hybrid framework for motion region detection and an appearance-based real-time motion tracking system. Initially, a foreground map is extracted through a process of subtraction from a background model, applying a temporal differencing method. Then, shadow elimination and morphological operations are used to remove noise. Finally, models are initiated for each detected motion region by extracting features such as center of mass and a color correlogram, which are then used for tracking purposes. As the similarity in distances within a certain radius is measured, the probability of confusing objects is reduced considerably, and therefore, performance is optimized significantly. The proposed framework also uses a robust technique to label people within a group. This framework has the capability to work in indoor, semi-outdoor, and even outdoor environments that generate a penumbra shadow, and it handles the groups formed due to occlusion effectively. The framework takes good care of false foreground pixels due to penumbra shadow. Hence, the proposed framework will play a pivotal role in providing security in highly confidential areas.

This paper is set to present a method of detecting motion that offers the possibility of creating more complex system for surveillance using image capture devices such as webcams, video cameras and others. A video surveillance system can start recording the images on a magnetic support, call the authorities or sound an alarm in case of detection of motion.

CONCEPTUAL AND SCIENTIFICALLY-METHODICAL PRINCIPLES OF REALIZATION OF POLICY IN THE FIELD OF THE STATE BORDER SECURITY IN UKRAINE, 2019

2015

Motion detection is a main task for video Surveillance system. In video surveillance, motion detection refers to the capability of the surveillance system to detect motion. Video surveillance is the procedure of finding a moving object or various objects over a period utilizing camera. Video Surveillance is a term given to monitor the behavior of any kind through videos. It requires person to monitor the CCTV and huge volume of memory to record it. One of the major challenges involved is the huge volume of video storage and retrieval of the same on demand. In order to avoid the depletion of human resources and to detect the suspicious behaviors that threaten safety and security, Intelligent Video Surveillance system (IVS) is required Because of key feature of video surveillance, it has a various uses like human-computer associations, security and surveillance, video communication, traffic control, open territories, for example, airports, underground stations, mass events, and so on....

Bashar M. Nema Recent studies have proved that a person cannot watch a static scene in a monitor for more than 20 minutes, therefore making traditional surveillance systems that rely on the presence of a person incompetent and unreliable. And With the development of computer technology, using computer to realize the human visual function through a computer has become one of the most popular subjects in computer field. So can be relying on intelligent video surveillance system for security purpose. Motion detection is first significant step in video surveillance systems, but there are various methods of motion detection, but which one can be use. In this paper, comparison is made between the popular methods are frame difference and background subtraction.

International Journal of Computer Applications, 2014

Real time moving object detection and tracking is one of the important research fields that have gained a lot of attention in the last few years. Tracking is required for security, safety and site management. Cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop technologies that automatically process those images in order to detect problematic situations or unusual behavior of human or object. Design computer vision base automated video surveillance system addresses real-time observation of object within a busy environment leading to the description of their actions and interactions. Object detection by background subtraction technique. Using single camera we detect and track human behavior. Background subtraction is the process of separating out the foreground objects from the background in a sequence of video frames. If human entity is cross the line design security in mall or public area the object is tracked. It is laborious to track and trace people over multiple cameras. In this paper, we present review for some system for real-time tracking and fast interactive retrieval of persons in video streams from single static surveillance camera.