Research On Key Techniques For Object Extraction In Low-frame-rate Image Sequences

With the development of scientific technology, image sequences become anextremely important data source and have been extensively applied in many fields, e.g.national defense, industry production, entertainment, media broadcast, and medicaldiagnosis, etc. Object extraction is to find and extract the space-time distribution objectsin image sequences. As a fundamental problem in image sequence analysis, objectextraction receives attentions from both scientists and engineers. Low-frame-rate imagesequences (frame rate≤5fps) have, for the past few years, been put into use in manycomplicated scenes, such as moving imaging platforms, wireless surveillance or limitedstorages. Low-frame-rate image sequences bring object extraction new challenges, e.g.,the long temporal interval between adjacent frames, weak spatiotemporal coherence,and intensive change in object appearance and scale.In this thesis, we focus on three key techniques for object extraction inlow-frame-rate image sequence object detection, object tracking and image sequenceobject segmentation. Object detection is the start point for object extraction; objecttracking explores the spatiotemporal coherence of objects; object segmentation obtainsspatiotemporal regions occupied by the objects. The main contributions of thedissertation are summarized as follows:1. To solve the problem of underutilization of the randomly extracted patches,we proposed a prescreening mechanism for the Hough forest based on therepresentation quality. The gray level spatial correlation histogram (GLSCH) wasintroduced and improved to characterize the randomly extracted patches. Then weemployed2D image entropy to measure the representation quality of the patches andconstructed the prescreening-based Hough forest. Extensive experiments on standarddatabase demonstrated the proposed pre-screening mechanism decreased the uncertaintyHough forest and improved the detection performance.2. We developed a novel scale invariant kernel-based object trackingalgorithm (SIKBOT) for tracking fast scaling objects in low-frame-rate imagesequences. We first proposed a novel set analysis based object similarity measure andthen employed the mean shift procedure to estimate the object scale. During eachiteration in tracking, object scale and object position were simultaneously estimated bytwo mean shift procedures in parallel. Compared with state-of-the-art methods, theproposed SIKBOT method improved the performance for tracking fast scaling objects.3. To accurately describe irregular-shaped objects during tracking, weproposed a new object-shape-based Epanechnikov kernel (shaped kernel, SK), whichwas then combined with the proposed SIKBOT algorithm to construct the shaped-kernelSIKBOT algorithm (SK+SIKBOT). The proposed shaped kernel can alleviate the influence of the background noise during object modeling. Moreover, the Epanechnikovprofile guarantees the strict convergence of the mean shift procedures. Extensiveexperiements demonstrated the proposed shaped kernel achieved improvements in bothaccuracy and efficiency.4. To overcome the error accumulation problem in low-frame-rate imagesequence object segmentation, we proposed a novel spatiotemporal Grab Cut algorithm.The object/background distribution propagation mechanism was established by tracking.Then by introducing the concepts of incomplete labeling and iterative estimation of theGrab Cut, we effectively alleviated the problem of error accumulation. Experimentalresults demonstrated the proposed spatiotemporal outperformed the state-of-the-artGraph Cuts-based image sequence object segmentation algorithms.