Computer Science Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2756
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Item Multi-Object Tracking, Event Modeling, and Activity Discovery in Video Sequences(2007-04-26) Joo, Seong-Wook; Chellappa, Rama; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)One of the main goals of computer vision is video understanding, where objects in the video are detected, tracked, and their behavior is analyzed. In this dissertation, several key problems in video understanding are addressed, focusing on video surveillance applications. Moving target detection and tracking is one of the most fundamental tasks in visual surveillance. A new moving target detection method is proposed where the temporal variance is used as a measure for characterizing object motion. Our method is experimentally shown to produce high detection rates while keeping low false positive rates. In tracking multiple objects, it is essential to correctly associate targets and measurements. We describe an efficient multi-object tracking approach that maintains multiple hypotheses over time regarding the association of targets and measurements. The data association problem is solved by a combinatorial optimization technique which finds the most likely association allowing track initiation, termination, merge, and split. Experimental results show that our method tracks through varying degrees of interactions among the targets with high success rate. Recognizing complex high-level events requires an explicit model of the structure of the events. Our approach uses attribute grammar for representing such event, which formally specifies the syntax of the symbols and the conditions on the attributes. Events are recognized using an extension of the Earley parser that handles attributes and concurrent event threads. Various examples of recognizing specific events of interest and detecting abnormal events are demonstrated using real data. Unsupervised methods for learning human activities have been largely based on clustering trajectories from a given scene. However, conventional clustering algorithms are not suitable for scenes that have many outlier trajectories. We describe a method for finding only salient groups of trajectories, using the probability of trajectories accidentally forming a group as the measure of significance of the group. The grouping algorithm finds groups that maximizes significance, while automatically determining the threshold for significance. We validate our approach on real data and analyze its performance using simulated data.Item Algorithms and evaluation for object detection and tracking in computer vision(2005-08-01) Kim, Kyungnam; Davis, Larry; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Vision-based object detection and tracking, especially for video surveillance applications, is studied from algorithms to performance evaluation. This dissertation is composed of four topics: (1) Background Modeling and Detection, (2) Performance Evaluation of Sensitive Target Detection, (3) Multi-view Multi-target Multi-Hypothesis Segmentation and Tracking of People, and (4) A Fine-Structure Image/Video Quality Measure. First, we present a real-time algorithm for foreground-background segmentation. It allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory. Our codebook-based representation is efficient in memory and speed compared with other background modeling techniques. Our method can handle scenes containing moving backgrounds or illumination variations, and it achieves robust detection for different types of videos. In addition to the basic algorithm, three features improving the algorithm are presented - Automatic Parameter Estimation, Layered Modeling/Detection and Adaptive Codebook Updating. Second, we introduce a performance evaluation methodology called Perturbation Detection Rate (PDR) analysis for measuring performance of foreground-background segmentation. It does not require foreground targets or knowledge of foreground distributions. It measures the sensitivity of a background subtraction algorithm in detecting possible low contrast targets against the background as a function of contrast. We compare four background subtraction algorithms using the methodology. Third, a multi-view multi-hypothesis approach to segmenting and tracking multiple persons on a ground plane is proposed. The tracking state space is the set of ground points of the people being tracked. During tracking, several iterations of segmentation are performed using information from human appearance models and ground plane homography. Two innovations are made in this chapter - (1) To more precisely locate the ground location of a person, all center vertical axes of the person across views are mapped to the top-view plane to find the intersection point. (2) To tackle the explosive state space due to multiple targets and views, iterative segmentation-searching is incorporated into a particle filtering framework. By searching for people's ground point locations from segmentations, a set of a few good particles can be identified, resulting in low computational cost. In addition, even if all the particles are away from the true ground point, some of them move towards the true one through the iterated process as long as they are located nearby. Finally, an objective no-reference measure is presented to assess fine-structure image/video quality. The proposed measure using local statistics reflects image degradation well in terms of noise and blur.Item Applications of Factorization Theorem and Ontologies for Activity ModelingRecognition and Anomaly Detection(2005-05-06) Akdemir, Umut; Chellappa, Rama; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis two approaches for activity modeling and suspicious activity detection are examined. First is application of factorization theorem extension for deformable models in two dierent contexts. First is human activity detection from joint position information, and second is suspicious activity detection for tarmac security. It is shown that the first basis vector from factorization theorem is good enough to dierentiate activities for human data and to distinguish suspicious activities for tarmac security data. Second approach dierentiates individual components of those activities using semantic methodol- ogy. Although currently mainly used for improving search and information retrieval, we show that ontologies are applicable to video surveillance. We evaluate the domain ontologies from Challenge Project on Video Event Taxonomy sponsored by ARDA from the perspective of general ontology design principles. We also focused on the eect of the domain on the granularity of the ontology for suspicious activity detection.