Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 77 for “"Action Recognition"”.
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Action recognition via sequence embedding
… embedding based approach is introduced for action recognition. We propose a new framework to represent an action as a weak classifier pool. During training, firstly, construct a set of static comb structural exemplars from training data; then convolve each exemplar on the training action …
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Action Recognition with Knowledge Transfer
… on deep neural networks has shown remarkable action recognition performance from videos. The remarkable performance is often achieved by transfer learning: training a model on a large-scale labeled dataset (source) and then fine-tuning the model on the small-scale labeled datasets (targets). …
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Towards practical automated human action recognition
… addressing high-level concepts such as humans' actions and activities. Automated human action recognition is an interesting research area, as well as one of the main trends in the automated video survei1lance industry. The typical goal of action recognition is that of labelling an image sequence …
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Human Action Recognition in Still Images
Recently still image-based human action recognition has become an active research topic in computer vision and pattern recognition. It focuses on identifying a person's action or behavior from a single image. Unlike the traditional action recognition approaches where videos or image sequences are …
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Design Optimizations for Action Recognition Applications
… that exist within the relatively new field of action recognition that make it difficult for the immediate use of existing models for specific applications. My work at the MIT-IBM Watson lab revolved around utilizing existing assets and optimizing performance for achieving action detection in …
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Kinect depth video compression for action recognition
… using a classification metric for human activity recognition. The first scheme uses the idea of companding to pre-process the data prior to compressing it with a standard H.264 coder. The second scheme uses a standard H.264 coder and appends additional feature bits to the compressed signal to aid …
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A biologically inspired system for action recognition
… present a biologically-motivated system for the recognition of actions from video sequences. The approach builds on recent work on object recognition based on hierarchical feedforward architectures and extends a neurobiological model of motion processing in the visual cortex. The system consists …
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Human action recognition with 3D convolutional neural networks
… recently developed elements to present a human action recognition model which is up-to-date with current trends in CNNs and current hardware. Focus is applied to ensemble models and methods such as the Dropout technique, developed by Hinton et al. (2012) to reduce overfitting, and learning rate …
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Hybrid architecture for human action recognition using skeleton data
… in skeleton based multi-person, multiview human action recognition. By leveraging attention-based GCN, the model captures context-dependent intrinsic topology while enhancing discriminative information. Furthermore, utilizing transformers, we harness their ability to aggregate long-range temporal …
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Vision-based human action recognition using machine learning techniques
The focus of this thesis is on automatic recognition of human actions in videos. Human action recognition is defined as automatic understating of what actions occur in a video performed by a human. This is a difficult problem due to the many challenges including, but not limited to, variations in …
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Human pose and action recognition using negative space analysis
… image space occupied by the body for pose and action recognition. The method proposed here, however, focuses on the negative spaces: the areas surrounding the individual. This has resulted in the colour-coded negative space approach, an image preprocessing step that circumvents the need for …
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Im2Vid: Future Video Prediction for Static Image Action Recognition
Static image action recognition aims at identifying the action performed in a given image. Most existing static image action recognition approaches use high-level cues present in the image such as objects, object human interaction, or human pose to better capture the action performed. Unlike …
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Human detection and action recognition using depth information by Kinect
… address the issues regarding human detection and action recognition. Taking the depth information, the basic problem we consider is to detect humans in the scene. We propose a model based approach, which is comprised of a 2D head contour detector and a 3D head surface detector. We propose a …
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Mid-level representations for action recognition and zero-shot learning
… We deal with the first problem in the task of action recognition and the other two problems in the task of zero-shot learning. For the first problem, we devise a representation suitable for characterising human actions on the basis of a sequence of pose estimates generated by an RGB-D sensor. …
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A study in human attention to guide computational action recognition
… heuristics for computational approaches to action recognition. I think that building a system modeled after human vision, with the nonuniform distribution of resolution and processing power, can greatly increase the performance of the computer systems that target action recognition. In this …
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Empirical Study on the Tradeoffs of Action Recognition Models for Industry
Action recognition has attracted intense attention in the last decade. Advances in deep learning and the availability of large-scale video datasets have drastically improved its capabilities, attracting interest from industry with a variety of use cases. My work at the MIT-IBM Watson AI lab …
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Automated Vision-Based Tracking and Action Recognition of Earthmoving Construction Operations
… for automated 2D tracking, 3D localization, and action recognition of construction equipment from different camera viewpoints is presented. In the proposed method, a new algorithm based on Histograms of Oriented Gradients and hue-saturation Colors (HOG+C) is used for 2D tracking of the …
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The dynamics of invariant object and action recognition in the human visual system
… recognize objects, and people and their actions from complex visual inputs. Despite the ease with which the human brain solves this problem, the underlying computational steps have remained enigmatic. What makes object and action recognition challenging are identity-preserving …
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Human action recognition in the real world: handling domain shift in open-set, source-free and multi-source scenarios
… in trending fields such as human-robot interaction, autonomous driving, drone footage, sports and video surveillance. The variety of different scenarios and conditions in which modern computer vision algorithms are expected to operate, along with the significant cost derived from collecting …
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Biomechanical Validation of Skeletal Tracking Data and Developing Action Recognition Models for Basketball: A Baseline for NBA Officiating Tools
… After cleaning the dataset of 117 NBA games, two action recognition models—a transformer-based model and a temporal graph neural network—are implemented to classify player actions, specifically dribbling, passing, shooting, and rebounding, from sequences of skeletal tracking frames. The objective …
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