About Some Methods of Motion Detection and Estimation: A Review

Journal of Electronic Imaging, 2017

The objective of this study is to compare several change detection methods for a mono static camera and identify the best method for different complex environments and backgrounds in indoor and outdoor scenes. To this end, we used the CDnet video dataset * as a benchmark that consists of many challenging problems, ranging from basic simple scenes to complex scenes affected by bad weather and dynamic backgrounds. Twelve change detection methods, ranging from simple temporal differencing to more sophisticated methods, were tested and several performance metrics were used to precisely evaluate the results. Because most of the considered methods have not previously been evaluated on this recent large scale dataset, this work compares these methods to fill a lack in the literature, and thus this evaluation joins as complementary compared with the previous comparative evaluations. Our experimental results show that there is no perfect method for all challenging cases; each method performs well in certain cases and fails in others. However, this study enables the user to identify the most suitable method for his or her needs.

2015

In today’s world, security of human being is the most active research area. Many different applications are being proposed to safeguard the public places. In this paper, we review the four different techniques of video surveillance system based on motion segmentation and tracking. The first system is based on dual frame differencing method followed by the morphological operations & Kalman filtering. The second technique is the use of visual background subtraction combined with illumination insensitive template matching algorithm. The third one is the optical flow used in combination of template matching. The final method is the design of AdaBoost classifier using sparse matrix & 450 rotated Haar features. This paper explores the different methods of visual tracking & their experimentation results to enhance the study in the field of image processing. Key-Words: Video Surveillance, Optical Flow, Frame Difference, AdaBoost Classifier.

2008

Motion segmentation is an essential process for many computer vision algorithms. During the last decade, a large amount of work has been trying to tackle this challenge, however, performances of most of them still fall far behind human perception. In this paper the motion segmentation problem is studied, analyzing and reviewing the most important and newest techniques. We propose a classification of all these techniques into different categories according to their main principle and features. Moreover, we point out their strengths and weaknesses and finally we suggest further research directions.

Iberoamerican Congress on Pattern Recognition CIARP, 2009

The use of image processing schemes as part of the security systems have been increasing, to detect, classify as well as to tract object and human motion with a high precision. To this end several approaches have been proposed during the last decades using image processing techniques, because computer vision let us to manipulated digital image sequences to extract useful

International Journal of Computer Applications, 2014

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.

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

2014

Extracting high level information is an important field in video indexing and retrieving. Discovering the presence of human in video is one of these high level features, which helps us to understand the other aspects of concerning people or the interactions between people. Our work focuses on a method for locating the presence of human in videos. The proposed system is able to detect the motion of human within frames from the video by means of image segmentation. Here we detect the background from the foreground image thus detecting the motion. The proposed work is crucial in lot of applications where the human detection is mandatory for preliminary steps. In this work, detection of motion is done using frames we can minimize the number of frames required to detect the movement thus minimizing the bandwidth. Keywords— Motion Detection, Image segmentation, Background Detection, Movement Detection

2013

Motion Detection and Segmentation in Dynamic Video Backgrounds Vivek Arya Amity University Haryana __________________________________________________________________________________________ Abstract--Nowadays roads are getting overcrowded, especially in metro cities. Hence the main aim of proposed research is to build a traffic monitoring system which replace or reduce the human monitoring system. The proposed technique is able to detect the movement of vehicles such as cars and to track the moving objects by analyzing a video. Moving object is detected using running average technique. The experimental results show that the proposed technique is adapted to monitor a road in metro cities, during cricket match to track the cricket ball and at the country’s borders for monitoring.

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.

Real-time detection of moving objects is vital for video surveillance. Background subtraction serves as a basic method typically used to segment the moving objects in image sequences taken from a camera. Some existing algorithms cannot fine-tune changing circumstances and they need manual calibration in relation to specification of parameters or some hypotheses for dynamic changing background. An adaptive motion segmentation and detection strategy is developed by using motion variation and chromatic characteristics, which eliminates undesired corruption of the background model and it doesn't look on the adaptation coefficient. In this particular proposed work, a novel real-time motion detection algorithm is proposed for dynamic changing background features. The algorithm integrates the temporal differencing along with optical flow method, double background filtering method and morphological processing techniques to achieve better detection performance. Temporal differencing is designed to detect initial motion areas for the optical-flow calculation to produce realtime and accurate object motion vectors detection. The double background filtering method is obtain and keep a reliable background image to handle variations on environmental changing conditions that is designed to get rid of the background interference and separate the moving objects from it. The morphological processing methods are adopted and mixed with the double background filtering to obtain improved results. The most attractive benefit for this algorithm is that the algorithm does not require to figure out the background model from hundreds of images and can handle quick image variations without prior understanding of the object size and shape.

2018

In todays world, object detection and tracking is much widespread and specially used for motion detection of various object. In object detection, the first step is to identify objects in the video sequence and cluster pixels of these objects. Classification of an object is the next important step to track the object. The object tracking can be applied in most of the fields that include computerized video surveillance, traffic monitoring, robotic vision, gesture identification, human-computer interaction, military surveillance system, vehicle navigation, medical imaging, biomedical image analysis and many more. The purpose of this work is to represent the various steps involved in tracking objects in 1 International Journal of Pure and Applied Mathematics Volume 118 No. 16 2018, 511-526 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu Special Issue ijpam.eu

Movement detection is a technology for detecting change in the surroundings relative to an object, Security systems which are being used now a days are not smart enough to provide real time notification after sensing the problem. To overcome this problem sensor based application can be used to view the activity and get notifications when the movement is detected, it saves the time and cost. This paper surveys various currently available techniques for movement detection based on different previously proposed system for motion detection.

2009

Detecting moving objects is very interested area in image processing. Motion can be detected by measuring changes in speed of an object or objects in the field of view. This system is connected with a fixed video camera and takes the video stream from it. Then the system compares the current image with a reference image and simply counts the number of different pixels. If the pixels of the resulted video frame are greater than the predefined alarm level, the system will fire the alarm event. The system use filters to implement the motion detection process. The main filters are difference filter – to find difference between two frames, threshold filter – to change the original frame to digital image, and erosion filter – to eliminate the noise. In this system, motion detector is implement to be very simple and efficiently. As the system doesn’t use the complex mathematical calculation, the process is very fast.

Elsevier, 2016

In the modern trends, intelligent video surveillance system is a very important and relevant topic of research. It is well suited for a broad range of applications which includes, video communication, security and surveillance,public areas such as airports,traffic control,monitoring activities at traffic intersections for detecting congestions, and then predicting the traffic flow which assists in regulating traffic, underground stations, mass events, etc. The main disadvantage of the system is,the storage space required for storing these data and retrieval of the same on demand. For these human resources are needed but manually reviewing the large amount of data often impractical. The proposed work focused on bringing effective and efficient system with intelligence to avoid human intervention in identifying security threats. In this paper, the moving objects in a video detected by using a method of optic flow with morphological operation.