Multiple Object Tracking and Segmentation in Video Sequences

BT Technology Journal, 2004

This paper aims to address two of the key research issues in computer vision -the detection and tracking of multiple objects in the cluttered dynamic scene -that underpin the intelligence aspects of advanced visual surveillance systems aiming at automated visual events detection and behaviour analysis. We discuss two major contributions in resolving these problems within a systematic framework. Firstly, for accurate object detection, an efficient and effective scheme is proposed to remove cast shadows/highlights with error corrections based on a conditional morphological reconstruction. Secondly, for effective tracking, a temporal-template-based tracking scheme is introduced, using multiple descriptive cues (velocity, shape, colour, etc) of the 2-D object appearance together with their respective variances over time. A scaled Euclidean distance is used as the matching metric, and the template is updated using Kalman filters when a matching is found or by linear mean prediction in the case of occlusion. Extensive experiments are carried out on video sequences from various realworld scenarios. The results show very promising tracking performance.

3rd CUTSE International Conference (CUTSE)

In the field of motion estimation for surveillance video, various techniques have been applied. One of the common approaches is Kalman filtering technique and it is interesting to explore the extension of this technique for the prediction and estimation of motion via the image sequences. In this paper, a moving object tracking in surveillance video using Kalman filter is proposed. The typical Kalman filter is good in tracking the position of a moving object. However, when dealing with occlusion, the typical Kalman filter is not able to keep tracking and predicting the position of the occluded moving object. During occlusion, the information of moving object is not available for detection and tracking. The lacking of occlusion scene determination and prediction ability cause the existing Kalman filter fails in tracking occluded object. Besides that, in the case of tracking multiple moving objects, existing Kalman filter will experience difficulties to identify the respective objects. Therefore, in order to encounter these problems, an object tracking method using enhanced Kalman filter will be developed. The ability of tracking occluded moving object will be added to increase the efficiency during tracking. Furthermore, object recognition feature will be added too to increase the accuracy of the object tracking system.

Emerging Trends in …, 2010

In this paper we propose multiple cameras using real time tracking for surveillance and security system. It is extensively used in the research field of computer vision applications, like that video surveillance, authentication systems, robotics, pre-stage of MPEG4 image compression and user inter faces by gestures. The key components of tracking for surveillance system are extracting the feature, background subtraction and identification of extracted object. Video surveillance, object detection and tracking have drawn a successful increased interest in recent years. A object tracking can be understood as the problem of finding the path (i.e. trajectory) and it can be defined as a procedure to identify the different positions of the object in each frame of a video. Based on the previous work on single detection using single stationary camera, we extend the concept to enable the tracking of multiple object detection under multiple camera and also maintain a security based system by multiple camera to track person in indoor environment, to identify by my proposal system which consist of multiple camera to monitor a person. Present study mainly aims to provide security and detect the moving object in real time video sequences and live video streaming. Based on a robust algorithm for human body detection and tracking in videos created with support of multiple cameras.

OBJECTS TRACKING IN A VIDEO SEQUENCE. This paper presents the result of implementing a tracking system for identifying objects in a video sequence. The main objective of this research is to keep track of objects movement and their activities which are then analyzed whether the activities related to suspicious activities or not. At this stage the research is concentrated on the keep track of the objects once the objects enter the scene. The objects tracking are done by identify objects' movement from video sequence using frame by frame analysis. In order to avoid tracking unnecessary objects a method is implemented to eliminate such objects. In this research a method to eliminate such objects is to use spatial objects information. Based on the described method the research shows that objects tracking in a video sequence can be implemented. Moreover, the research is also trying to isolate objects so that the object size and its activities can be analyzed. Finally, this research h...

2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), 2015

Object tracking is the process of locating moving objects over time using the camera in video sequences. The objective of object tracking is to associate target objects in consecutive video frames. Object tracking requires location and shape or features of objects in the video frames. So, object detection and object classification is the preceding steps of object tracking in computer vision application. To detect or locate the moving object in frame, Object detection is first stage in tracking. After that, detected object can be classified as vehicles, human, swaying tree, birds and other moving objects. It is challenging or difficult task in the image processing to track the objects into consecutive frames. Various challenges can arise due to complex object motion, irregular shape of object, occlusion of object to object and object to scene and real time processing requirements. Object tracking has a variety of uses, some of which are: surveillance and security, traffic monitoring, video communication, robot vision and animation. This paper presents the various techniques of object tracking in video sequences through different phases using image processing.

IOSR Journal of Engineering, 2012

Network video surveillance has been a popular security application for many years. Target tracking in a cluttered environment remains one of the challenging problems of video surveillance. The task of target tracking is a key component of video surveillance and monitoring systems. It provides input to high-level processing such as recognition, access control or re-identification or is used to initialize the analysis and classification of human activities. Intelligent and automated security surveillance systems have become an active research area in recent time due to an increasing demand for such systems in public areas such as airports, underground stations and mass events. In this context, tracking of stationary foreground regions is one of the most critical requirements for surveillance systems based on the tracking of abandoned or stolen objects or parked vehicles. Object tracking based techniques are the most popular choice to detect stationary foreground objects because they work reasonably well when the camera is stationary and the change in ambient lighting is gradual, and they also represent the most popular choice to separate foreground objects from the current frame. In this paper, we did the literature survey on different technique and finally carried out our methodology for the same situation.

Image and Vision Computing, 2006

In this paper, a novel image segmentation and a robust unsupervised video objects tracking algorithm are proposed. The proposed method is able to track complete object regions in a sequence of video frames. In this work, object tracking is achieved by analysing the movement of the contours with frame by frame in the video stream. The proposed algorithm involves with three major components for analysing the shapes and motions of the object in the video frames. First, a modified mathematical morphology edge detection algorithm is utilized to extract the contour features in the video frames. Then, a contour-based image segmentation algorithm is proposed and applied to the contour features for partitioning the predetermined target objects in the video frames. Finally, a trajectory estimation scheme is developed to handle the movements of the objects in the video frames. The proposed image segmentation algorithm is capable of automatically partitioning the predetermined objects. The proposed tracking algorithm is also robust against overlapping and videos acquired by non-stationary cameras. The experimental results show that the proposed algorithm can precisely partition and track the predetermined objects in video frames.

Segmentation and tracking are two important aspects in visual surveillance systems. Many barriers such as cluttered background, camera movements, and occlusion make the robust detection and tracking a difficult problem, especially in case of multiple moving objects. Object detection in the presence of camera noise and with variable or unfavourable luminance conditions is still an area of active research. In this paper, a survey of various techniques or methods that are used to segment, detect and track objects in the surveillance videos with stationary and complex backgrounds, crowded area, multi-modality background, occluded object, and deformable based objects is provided. For handling complex backgrounds, multi-background registration based segmentation is available. Various techniques used for segmentation based on frame differencing and background modelling are included.

International Journal of Computer Applications, 2017

Computer Vision (CV) concentrates on the automatic extraction, examination and comprehension of valuable data from a solitary image or a group of images. Object tracking, one of the key areas in CV has received a lot of attenstion in recent times. Tracking objects is a systematic process of monitoring the movement of a target object from its initial state to the nth state over a period of time using a camera. This technique is usually employed as a security feature in both military and civilian systems. However, prior studies has shown that tracking objects in motion is a very difficult task and is a hot research hotspot in the field of computer vision and machine learning. In this review paper we discuess various techniques in detection, tracking and some other related works of moving objects in video streams.

2016

Detecting and tracking objects in crowded areas is a challenging issue in the field of Video Surveillance System. Nowadays the increase of digital video cameras, and the availability of video storage and high performance video processing hardware, opens up conceivable outcomes for tackling many video understanding problems. Developing a real-time video understanding technique which can process the large amounts of data becomes very important. The object detection first step used in surveillance applications aims to separation of foreground objects from the background. Many algorithms proposed to solve the problem of object detection, however, it still lack of tracking multiple objects in real time. Object tracking used to find a moving object detected in motion detection stage from one frame to another in an image sequence. This paper focuses on review of various techniques used in object detection and object tracking.