Object Tracking In Video Surveillance Research Articles

A Review on the Identification and Classification of Activities for the Purpose of Tracking Objects in Video Surveillance

An increasing number of academics are becoming interested in the field of image processing since object tracking in video surveillance is a significant application and a growing subject of research in the field of technique known as video tracking is one that involves the utilization of a camera in order to find a moving object or many moving things throughout the course of time. As a result of its key qualities, video surveillance serves a variety of functions, including but not limited to human-computer interactions, security and surveillance, video communication, traffic management, and public areas such as airports, underground stations, and large-scale events, amongst others. Keeping track of a target in a congested area is still a difficult video surveillance task. The processing framework for video surveillanceis completed by a sequential flow that includes the detection of moving objects, their classification, tracking, and behavior identification. This study examines tracking techniques, classifies them into many categories, and concentrates on significant and practical tracking techniques. This paper discusses several tracking systems, including active contour-based, area-based, and others, along with their advantages and disadvantages. Various tracking techniques are discussed along with thorough descriptions. Finally, researchers presentan overview of potential research areas after reviewing general tactics and a scan of the literature on various methodologies.

Modified block-matching algorithm for moving object tracking in video surveillance

Video surveillance has risen as one of the most promising methods for people who live alone in their dwellings. Few video surveillance innovations have recently been introduced. However, due to various changes in illumination, abrupt shifts in target appearance, identical non-target artifacts in the background, and occlusions, developing a reliable video surveillance algorithm remains a difficult challenge. This work attempts to introduce a new framework for moving object detection and tracking by following four major phases: “Video-to-Frame Conversion, Pre-Processing, Background Subtraction, Feature-Based Multi-object Detection, Multi-object Tracking by Filtering”. Initially, in the Video-to-Frame Conversion process, the recorded input video clips are transformed into distinct frames. During pre-processing, the noise is removed from the video frame using a filtering approach, and thereby the nature of the images will be enhanced. In the proposed work, a Weiner filter is used to remove noise and other undesirable features during the pre-processing. Then, to distinguish the frontal areas of objects, background subtraction is performed using the neutrosophic set in noiseless video frames (pre-processed frames). The objects in the background-subtracted frames are separated using Improved Region Growing (IRG) segmentation model in the Feature-Based Multi-object Detection phase. The objects in the frames are determined from this segmented image. The Modified Full Search Algorithm is being used to track the object (motion estimation) on the video frame after it has been identified in the segmented phase. The Modified full search block matching algorithm (MFSA) is introduced in this research work to find the appropriate mobility. Promising results have been obtained by the proposed work, and also the mathematical excellence of the new method is also proven over other state-of-the-art models.

Development of a Novel Algorithm for Moving Object Tracking in Video Surveillance

Video surveillance is a process of analyzing video sequences. It involves analysis, interpretation of object behaviors, as well as object detection and tracking. Video processing plays an important role in the industry and computer vision such as online monitoring of assembly processes, video surveillance security system, medical treatment, robot navigation and military, etc. Detection and tracking of human objects is one of the important studies in improving the ability of the surveillance system. The aim of this research work is to measure and analyze the application of background subtraction method and block matching algorithm to trace object movements through video-based. This research applies background subtraction method to detect moving object, assisted with block matching algorithm which aims to get good results on objects that have been detected. Performance evaluation is carried out to determine the various parameters. In this paper author design and develop a novel algorithm for moving object tracking in video surveillance also compares and analyse existing algorithms for moving object tracking. Author main aim to design and develop an algorithm for moving object tracking to handle occlusion and complex object shapes.

Moving Object Tracking Algorithm for Complex Shape and Occlusion Handling

An object tracking increases loads of enthusiasm for dynamic research in applications such as video surveillance, vehicle navigation, highways, crowded public places, borders, forest and traffic monitoring areas. The system we develop aims to measure and analyze the application of background subtraction method and block matching algorithm to trace object movements through video-based. The making of video surveillance systems “smart” requires fast, reliable and robust algorithms for moving object detection and tracking. This research applies background subtraction method to detect moving object, assisted with block matching algorithm which aims to get good results on objects that have been detected. Performance evaluation is carried out to determine the various parameters. In this paper author design and develop a novel algorithm for moving object tracking in video surveillance also compares and analyse existing algorithms for moving object tracking. Author main aim to design and develop an algorithm for moving object tracking to handle occlusion and complex object shapes.

Robust Visual Multi Target Trackers: A Review

<p>In this review paper, we address on the state-of-art process with visual object tracking in video surveillance, medical and military applications. In the present scenario number of algorithms and methods are used to track the object in the different scene, a robust visual object tracking remains a critical challenge. The challenges arise due to object motion from frame to frame with a change in appearance, structures, illumination, and occlusion. In this paper, at last, we outline the different algorithms, dataset, strength, and weakness of the different object tracker.</p>

An enhanced framework for multimedia data: Green transmission and portrayal for smart traffic system

The object tracking in video surveillance for intelligent traffic handling in smart cities requires an enormous amount of data called big data to be transmitted over the network using the Internet of Things. Manual monitoring and surveillance are impossible because traditional computer vision technologies are no more useful for massive processing and intelligent decision making. In this paper, a framework is proposed which enables both on spot data processing and intelligent decision making by using cloud computing. The developed application is a trained on Artificial Neural Network, which can handle different traffic techniques with congested traffic scenario and priorities traffic such as ambulance handling. The Message Queue Telemetry Transport protocol is used for green transmission with mobile access to traffic data. The results analyzed with thirty videos processed data which handle real-time data prioritization for the people for smart surveillance to fastest route and enhance the intelligent data transmission.

The Research and Implementation of Camshift Algorithm Based on Automatically Target Extraction

Camshift algorithm is often used for object tracking in video surveillance, which has a good real-time performance. Unfortunately, Camshift is a semi-automatic tracking algorithm, it needs to select the initial search window by manual, and the manual-selection will result in a target-selection bias. For this shortcoming, this paper presents an improved Camshift algorithm to automatically select a moving target. Firstly, the moving object is extracted by background subtraction and threeframe-differences method, then the location of extracted moving target will be taken as the initial search window for the Camshift algorithm, and finally the automatic tracking of the moving target is realized.