A Review on Motion Detection Techniques

International Journal of Computer Applications, 2014

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....

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.

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.

IAEME PUBLICATION, 2012

Detection of moving objects from video frames plays an important and often very critical role in different kinds of machine vision applications including human detection and tracking, traffic monitoring and military applications. A common way to detect moving objects is background subtraction. In background subtraction, moving objects are detected by comparing each video frame against an existing model of the scene background. In this paper, we proposed an axis based algorithm for detection of moving objects. The algorithm is based on position of pixels according to x axis and y axis. Each pixel in an image takes some value. The algorithm operates in real-time under the assumption of a stationary camera. It can handle all multiple backgrounds because it does not follow the concept of background and foreground. It only depends upon the movement of pixel according to x axis and y axis. This algorithm also reduces time (T) taken in detection of moving objects.

IJMER

Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-capacity storage devices and high speed network infrastructure paved the way for cheaper, multi sensor video surveillance systems. Traditionally, the video outputs are processed online by human operators and are usually saved to tapes for later use only after a forensic event. The increase in the number of cameras in ordinary surveillance systems overloaded both the human operators and the storage devices with high volumes of data and made it infeasible to ensure proper monitoring of sensitive areas for long times. In order to filter out redundant information generated by an array of cameras, and increase the response time to forensic events, assisting the human operators with identification of important events in video by the use of “smart” video surveillance systems has become a critical requirement. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis.

International Conference on Image Analysis and Processing, 2007

Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient.

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.

SN Computer Science, 2020

Moving object detection and tracking from video sequences are a relevant research field since it can be used in many applications. While detection allows to return object shapes discovered in the image, tracking aims to individually identify and estimate individual trajectories of detected objects over time. Hence, detection can have a crucial impact on the overall tracking process. This paper focuses on detection. Currently, one of the leading detection algorithms includes frame difference method (FD), background subtraction method (BS), and optical flow method. Here, we present a detection algorithm based on the first two approaches since it is very adequate for fast real-time treatments, whereas optical flow has higher computation cost due to a dense estimation. A combination of FD and BS with Laplace filters and edge detectors is a way to achieve sparse detection fast. Thus, a main proposed contribution is the achievement of a systematic detection algorithm for moving target detection with a more elaborated combination of basic procedures used in real-time surveillance. Experimental results show that the proposed method has higher detection accuracy and better noise suppression than the current methods for standard benchmark datasets.

Moving object detection has been widely used in diverse discipline such as intelligent transportation systems, airport security systems, video monitoring systems, and so on. In this paper, we propose an efficient moving object detection method using enhanced edge localization mechanism and gradient directional masking for video surveillance system. In our proposed method, gradient map images are initially generated from the input and background images using a gradient operator. The gradient difference map is then calculated from gradient map images. The moving object is then detected by using appropriate directional masking and thresholding. Simulation results indicate that the proposed method consistently performs well under different illumination conditions including indoor, outdoor, sunny, and foggy cases. Moreover, it outperforms well known edge based method in terms of detecting moving objects and error rate. Moreover, the proposed method is computationally faster and it is applicable for detecting moving object in real-time.