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Analyzing Complex Events and Human Actions in "in-the-wild" Videos

dc.contributor.advisorDavis, Larry Sen_US
dc.contributor.authorLee, Hyungtaeen_US
dc.date.accessioned2014-10-16T05:38:14Z
dc.date.available2014-10-16T05:38:14Z
dc.date.issued2014en_US
dc.identifierhttps://doi.org/10.13016/M2HG7S
dc.identifier.urihttp://hdl.handle.net/1903/15908
dc.description.abstractWe are living in a world where it is easy to acquire videos of events ranging from private picnics to public concerts, and to share them publicly via websites such as YouTube. The ability of smart-phones to create these videos and upload them to the internet has led to an explosion of video data, which in turn has led to interesting research directions involving the analysis of ``in-the-wild'' videos. To process these types of videos, various recognition tasks such as pose estimation, action recognition, and event recognition become important in computer vision. This thesis presents various recognition problems and proposes mid-level models to address them. First, a discriminative deformable part model is presented for the recovery of qualitative pose, inferring coarse pose labels (e:g: left, front-right, back), a task more robust to common confounding factors that hinder the inference of exact 2D or 3D joint locations. Our approach automatically selects parts that are predictive of qualitative pose and trains their appearance and deformation costs to best discriminate between qualitative poses. Unlike previous approaches, our parts are both selected and trained to improve qualitative pose discrimination and are shared by all the qualitative pose models. This leads to both increased accuracy and higher efficiency, since fewer parts models are evaluated for each image. In comparisons with two state-of-the-art approaches on a public dataset, our model shows superior performance. Second, the thesis proposes the use of a robust pose feature based on part based human detectors (Poselets) for the task of action recognition in relatively unconstrained videos, i.e., collected from the web. This feature, based on the original poselets activation vector, coarsely models pose and its transitions over time. Our main contributions are that we improve the original feature's compactness and discriminability by greedy set cover over subsets of joint configurations, and incorporate it into a unified video-based action recognition framework. Experiments shows that the pose feature alone is extremely informative, yielding performance that matches most state-of-the-art approaches but only using our proposed improvements to its compactness and discriminability. By combining our pose feature with motion and shape, the proposed method outperforms state-of-the-art approaches on two public datasets. Third, clauselets, sets of concurrent actions and their temporal relationships, are proposed and explored their application to video event analysis. Clauselets are trained in two stages. Initially, clauselet detectors that find a limited set of actions in particular qualitative temporal configurations based on Allen's interval relations is trained. In the second stage, the first level detectors are applied to training videos, and discriminatively learn temporal patterns between activations that involve more actions over longer durations and lead to improved second level clauselet models. The utility of clauselets is demonstrated by applying them to the task of ``in-the-wild'' video event recognition on the TRECVID MED 11 dataset. Not only do clauselets achieve state-of-the-art results on this task, but qualitative results suggest that they may also lead to semantically meaningful descriptions of videos in terms of detected actions and their temporal relationships. Finally, the thesis addresses the task of searching for videos given text queries that are not known at training time, which typically involves zero-shot learning, where detectors for a large set of concepts, attributes, or objects parts are learned under the assumption that, once the search query is known, they can be combined to detect novel complex visual categories. These detectors are typically trained on annotated training data that is time-consuming and expensive to obtain, and a successful system requires many of them to generalize well at test time. In addition, these detectors are so general that they are not well-tuned to the specific query or target data, since neither is known at training. Our approach addresses the annotation problem by searching the web to discover visual examples of short text phrases. Top ranked search results are used to learn general, potentially noisy, visual phrase detectors. Given a search query and a target dataset, the visual phrase detectors are adapted to both the query and unlabeled target data to remove the influence of incorrect training examples or correct examples that are irrelevant to the search query. Our adaptation process exploits the spatio-temporal coocurrence of visual phrases that are found in the target data and which are relevant to the search query by iteratively refining both the visual phrase detectors and spatio-temporally grouped phrase detections (`clauselets'). Our approach is demonstrated on to the challenging TRECVID MED13 EK0 dataset and show that, using visual features alone, our approach outperforms state-of-the-art approaches that use visual, audio, and text (OCR) features.en_US
dc.language.isoenen_US
dc.titleAnalyzing Complex Events and Human Actions in "in-the-wild" Videosen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentElectrical Engineeringen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pqcontrolledInformation scienceen_US
dc.subject.pqcontrolledInformation technologyen_US
dc.subject.pquncontrolledAction recognitionen_US
dc.subject.pquncontrolledClauseleten_US
dc.subject.pquncontrolledEvent recognitionen_US
dc.subject.pquncontrolledPose estimationen_US
dc.subject.pquncontrolledQualitative poseen_US


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