Object Detection and Tracking using Watershed Segmentation and KLT Tracker

In this paper a moving object is extracted from a video using video object detection algorithm based on spatial and temporal segmentation The technique begins with temporal segmentation in which edge map is extracted using edge operator The initial binary mask is obtained by using morphological operation applied on initial edge map The next phase is spatial segmentation where gradient image is obtained by multi-scale morphological operator The modified gradient image is obtained by the operator applied over the current frame At last moving object is extracted by precisely and accurately by watershed segmentation which is performed on the modified gradient image Again morphological operation is applied on the output to get final binary mask This binary mask is then complemented to yield the contour line of the video object Using the binary mask the video object is extracted from the video frames After detection of video object the object tracking is performed using Kanade Lucas Tomasi KLT feature tracke

Video object segmentation.

Wei Wei.Thesis submitted in: December 2005.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 112-122).Abstracts in English and Chinese.Abstract --- p.IIList of Abbreviations --- p.IVChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview of Content-based Video Standard --- p.1Chapter 1.2 --- Video Object Segmentation --- p.4Chapter 1.2.1 --- Video Object Plane (VOP) --- p.4Chapter 1.2.2 --- Object Segmentation --- p.5Chapter 1.3 --- Problems of Video Object Segmentation --- p.6Chapter 1.4 --- Objective of the research work --- p.7Chapter 1.5 --- Organization of This Thesis --- p.8Chapter 1.6 --- Notes on Publication --- p.8Chapter Chapter 2 --- Literature Review --- p.10Chapter 2.1 --- What is segmentation? --- p.10Chapter 2.1.1 --- Manual Segmentation --- p.10Chapter 2.1.2 --- Automatic Segmentation --- p.11Chapter 2.1.3 --- Semi-automatic segmentation --- p.12Chapter 2.2 --- Segmentation Strategy --- p.14Chapter 2.3 --- Segmentation of Moving Objects --- p.17Chapter 2.3.1 --- Motion --- p.18Chapter 2.3.2 --- Motion Field Representation --- p.19Chapter 2.3.3 --- Video Object Segmentation --- p.25Chapter 2.4 --- Summary --- p.35Chapter Chapter 3 --- Automatic Video Object Segmentation Algorithm --- p.37Chapter 3.1 --- Spatial Segmentation --- p.38Chapter 3.1.1 --- k:-Medians Clustering Algorithm --- p.39Chapter 3.1.2 --- Cluster Number Estimation --- p.41Chapter 3.1.2 --- Region Merging --- p.46Chapter 3.2 --- Foreground Detection --- p.48Chapter 3.2.1 --- Global Motion Estimation --- p.49Chapter 3.2.2 --- Detection of Moving Objects --- p.50Chapter 3.3 --- Object Tracking and Extracting --- p.50Chapter 3.3.1 --- Binary Model Tracking --- p.51Chapter 3.3.1.2 --- Initial Model Extraction --- p.53Chapter 3.3.2 --- Region Descriptor Tracking --- p.59Chapter 3.4 --- Results and Discussions --- p.65Chapter 3.4.1 --- Objective Evaluation --- p.65Chapter 3.4.2 --- Subjective Evaluation --- p.66Chapter 3.5 --- Conclusion --- p.74Chapter Chapter 4 --- Disparity Estimation and its Application in Video Object Segmentation --- p.76Chapter 4.1 --- Disparity Estimation --- p.79Chapter 4.1.1. --- Seed Selection --- p.80Chapter 4.1.2. --- Edge-based Matching by Propagation --- p.82Chapter 4.2 --- Remedy Matching Sparseness by Interpolation --- p.84Chapter 4.2 --- Disparity Applications in Video Conference Segmentation --- p.92Chapter 4.3 --- Conclusion --- p.106Chapter Chapter 5 --- Conclusion and Future Work --- p.108Chapter 5.1 --- Conclusion and Contribution --- p.108Chapter 5.2 --- Future work --- p.109Reference --- p.11

Unsupervised offline video object segmentation using object enhancement and region merging

Content-based representation of video sequences for applications such as MPEG-4 and MPEG-7 coding is an area of growing interest in video processing. One of the key steps to content-based representation is segmenting the video into a meaningful set of objects. Existing methods often accomplish this through the use of color, motion, or edge detection. Other approaches combine several features in an effort to improve on single-feature approaches. Recent work proposes the use of object trajectories to improve the segmentation of objects that have been tracked throughout a video clip. This thesis proposes an unsupervised video object segmentation method that introduces a number of improvements to existing work in the area. The initial segmentation utilizes object color and motion variance to more accurately classify image pixels to their best fit region. Histogram-based merging is then employed to reduce over-segmentation of the first frame. During object tracking, segmentation quality measures based on object color and motion contrast are taken. These measures are then used to enhance video objects through selective pixel re-classification. After object enhancement, cumulative histogram-based merging, occlusion handling, and island detection are used to help group regions into meaningful objects. Objective and subjective tests were performed on a set of standard video test sequences which demonstrate improved accuracy and greater success in identifying the real objects in a video clip compared to two reference methods. Greater success and improved accuracy in identifying video objects is first demonstrated by subjectively examining selected frames from the test sequences. After this, objective results are obtained through the use of a set of measures that aim at evaluating the accuracy of object boundaries and temporal stability through the use of color, motion and histogram

Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters

Segmentation of an object from a video is a challenging task in multimedia applications. Depending on the application, automatic or interactive methods are desired; however, regardless of the application type, efficient computation of video object segmentation is crucial for time-critical applications; specifically, mobile and interactive applications require near real-time efficiencies. In this paper, we address the problem of video segmentation from the perspective of efficiency. We initially redefine the problem of video object segmentation as the propagation of MRF energies along the temporal domain. For this purpose, a novel and efficient method is proposed to propagate MRF energies throughout the frames via bilateral filters without using any global texture, color or shape model. Recently presented bi-exponential filter is utilized for efficiency, whereas a novel technique is also developed to dynamically solve graph-cuts for varying, non-lattice graphs in general linear filtering scenario. These improvements are experimented for both automatic and interactive video segmentation scenarios. Moreover, in addition to the efficiency, segmentation quality is also tested both quantitatively and qualitatively. Indeed, for some challenging examples, significant time efficiency is observed without loss of segmentation quality.Comment: Multimedia, IEEE Transactions on (Volume:16, Issue: 5, Aug. 2014

Segmentation and tracking of video objects for a content-based video indexing context

This paper examines the problem of segmentation and tracking of video objects for content-based information retrieval. Segmentation and tracking of video objects plays an important role in index creation and user request definition steps. The object is initially selected using a semi-automatic approach. For this purpose, a user-based selection is required to define roughly the object to be tracked. In this paper, we propose two different methods to allow an accurate contour definition from the user selection. The first one is based on an active contour model which progressively refines the selection by fitting the natural edges of the object while the second used a binary partition tree with aPeer ReviewedPostprint (published version

Illumination invariant stationary object detection

A real-time system for the detection and tracking of moving objects that becomes stationary in a restricted zone. A new pixel classification method based on the segmentation history image is used to identify stationary objects in the scene. These objects are then tracked using a novel adaptive edge orientation-based tracking method. Experimental results have shown that the tracking technique gives more than a 95% detection success rate, even if objects are partially occluded. The tracking results, together with the historic edge maps, are analysed to remove objects that are no longer stationary or are falsely identified as foreground regions because of sudden changes in the illumination conditions. The technique has been tested on over 7 h of video recorded at different locations and time of day, both outdoors and indoors. The results obtained are compared with other available state-of-the-art methods

A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks benefit from decomposing a graphical model into an optimal number of connected components based on attractive and repulsive pairwise terms. The two tasks are formulated on different levels of granularity and, accordingly, leverage mostly local information for motion segmentation and mostly high-level information for multi-target tracking. In this paper we argue that point trajectories and their local relationships can contribute to the high-level task of multi-target tracking and also argue that high-level cues from object detection and tracking are helpful to solve motion segmentation. We propose a joint graphical model for point trajectories and object detections whose Multicuts are solutions to motion segmentation {\it and} multi-target tracking problems at once. Results on the FBMS59 motion segmentation benchmark as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark demonstrate the promise of this joint approach

An improved background segmentation method for ghost removals

With ongoing research assessment in higher education and the introduction of master’s‐level work in initial teacher education, the growing need for teacher educators to develop research identities is discussed in relation to mentoring and support in two universities. Twelve interviews—with three teacher educators and three research mentors from each university—were carried out, in order to identify effective mentoring practices and other forms of support, as well as any barriers or problems encountered in developing a research profile. An innovative aspect of the methodological approach is that beginning researchers from the teacher education faculty in both universities undertook the interviewing and co‐authored the article. The need for an entitlement to and protection of research time is stressed, as well as a range of supportive practices within an active research culture. It is argued that this aspect of teacher educators’ professional development requires as much attention as the pedagogical aspects of their rol