Multiple Objects Tracking across Multiple Non-Overlapped Views

Motion and Video Computing, 2002. …, 2002

This paper presents a set of methods for multi view image tracking using a set of calibrated cameras. We demonstrate how effective the approach is for resolving occlusions and tracking objects between overlapping and non-overlapping camera views. Moving objects are ...

2017

During the past decade, object detection and object tracking in videos have received a great deal of attention from the research community in view of their many applications, such as human activity recognition, human computer interaction, crowd scene analysis, video surveillance, sports video analysis, autonomous vehicle navigation, driver assistance systems, and traffic management. Object detection and object tracking face a number of challenges such as variation in scale, appearance, view of the objects, as well as occlusion, and changes in illumination and environmental conditions. Object tracking has some other challenges such as similar appearance among multiple targets and long-term occlusion, which may cause failure in tracking. Detection-based tracking techniques use an object detector for guiding the tracking process. However, existing object detectors usually suffer from detection errors, which may mislead the trackers, if used for tracking. Thus, improving the performance...

2009

ABSTRACT Tracking moving objects is a critical step for smart video surveillance systems. Despite the complexity increase, multiple camera systems exhibit the undoubted advantages of covering wide areas and handling the occurrence of occlusions by exploiting the different viewpoints. The technical problems in multiple camera systems are several: installation, calibration, objects matching, switching, data fusion, and occlusion handling.

2018

Multiple Object Tracking is the process of locating multiple objects over time in a video stream. Object detection and classification are two prior steps before performing tracking over video scene. Object detection is the process of locating an object of interest in a single frame. So, in other words we can say that multiple object tracking is the process of associating detected objects in consecutive video frames. The detected objects may belong to various categories such as vehicles, humans, swaying trees or other moving objects. So, object classification is the process to classify these objects using different approaches. However, some object tracking applications may not need to classify detected objects. In this paper, we had discussed various object detection and tracking methods, which are available in the literature.

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.

long term occlusion is a most important challenge in any multiple objects tracking system. This paper presents a literature survey of an object tracking algorithms in a fixed camera situation that have been used by others to address the long term occlusion problem. Based on this assessment of the state of the art, this paper identify what appears to be the most promising algorithm for long term occlusion that was succeeds in handling interacting objects of similar appearance without any strong assumptions on the characteristics of the tracked objects. But this algorithm failed to handle objects of too complex shapes and appearance, and the tracking results was affected by successfully of background subtraction. This paper presents a proposed solution to these failed points.

2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007

We propose a framework for detecting and tracking multiple interacting objects, while explicitly handling the dual problems of fragmentation (an object may be broken into several blobs) and grouping (multiple objects may appear as a single blob). We use foreground blobs obtained by background subtraction from a stationary camera as measurements. The main challenge is to associate blob measurements with objects, given the fragment-object-group ambiguity when the number of objects is variable and unknown, and object-class-specific models are not available. We first track foreground blobs till they merge or split. We then build an inference graph representing merge-split relations between the tracked blobs. Using this graph and a generic object model based on spatial connectedness and coherent motion, we label the tracked blobs as whole objects, fragments of objects or groups of interacting objects. The outputs of our algorithm are entire tracks of objects, which may include corresponding tracks from groups during interactions. Experimental results on multiple video sequences are shown.

International Journal of Computer Applications

Multiple object tracking is being used for many applications nowdays such as automated surveillance, Robotics,self driving cars,medical and many more. There have been continuous improvements in existing state of art MOT(multiple object tracking) methods through many methods and global optimization techniques.This paper focuses on various MOT techniques and how to achieve speedup and efficiency using MOT methods.

2005

Abstract We present an algorithm for tracking video objects which is based on a hybrid strategy. This strategy uses both object and region information to solve the correspondence problem. Low-level descriptors are exploited to track object's regions and to cope with track management issues. Appearance and disappearance of objects, splitting and partial occlusions are resolved through interactions between regions and objects.

2001

Abstract We address the problem of reliable real-time 3D-tracking of multiple objects which are observed in multiple wide-baseline camera views. Establishing the spatio-temporal correspondence is a problem with combinatorial complexity in the number of objects and views. In addition vision based tracking suffers from the ambiguities introduced by occlusion, clutter and irregular 3D motion.

EURASIP Journal on Advances in Signal Processing, 2002

We propose a method for tracking of objects contained in video sequences. Each video object is represented by a set of polygonal regions. A bottom up approach (spatial segmentation/motion estimation) is applied for the initialisation of the method, a limited human interaction is used to build the semantic map of the first frame in video sequence. The tracking of this model along a video sequence is based on detecting and indexing new objects in a video scene. Semantic rules are used to label new objects and, the current state of segmentation is validated by forward projection of the background.

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.

Pattern Recognition, 2007

This paper describes a technique that produces a content-based representation of a video shot composed by a background (still) mosaic and one or more foreground moving objects. Segmentation of moving objects is based on ego-motion compensation and on background modelling using tools from robust statistics. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences ...

Multimedia Systems, 2017

International Journal of Computer Applications

Visual Object Tracking (VOT) is the most salient and an ongoing exploration field amongst the several disciplines of computer vision. The importance of this technology is due to the extensive range of applications such as robot navigation, human computer interaction, video surveillance, etc. The process of object tracking involves segmenting areas of a video scene and tracking its position, motion and occlusion. However, problems can appear during tracking on account of multiple issues including camera motion, object-to-object and object-to-scene occlusions, nonrigid structures, object and scene changes in patterns and appearance and abrupt object movement. The aim of this paper is to examine, analyze and provide a shortlist of the most ubiquitous object tracking techniques. This accomplish by providing a comprehensive review of the tracking process which involve object detection methods, object representation and features selection and object tracking over multiple frames. Object tracking methods are compared whilst elaborating upon the advantages and limitations.