6,813 research outputs found

    A pair hidden Markov support vector machine for alignment of human actions

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    © 2016 IEEE. Alignment of human actions in videos is an important task for applications such as action comparison and classification. While well-established algorithms such as dynamic time warping are available for this task, they still heavily rely on basic linear cost models and heuristic parameter tuning. In this paper we propose a novel framework that combines the flexibility of the pair hidden Markov model (PHMM) with the effective parameter training of the structural support vector machine (SSVM). The framework extends the scoring function of SSVM to capture the similarity of two input sequences and introduces suitable feature and loss functions. The proposed approach is evaluated against state-of-the-art algorithms such as dynamic time warping (DTW) and canonical time warping (CTW) on pairs of human actions from the Weizmann and Olympic Sports datasets. The experimental results show that the proposed approach is capable of achieving an accuracy improvement of over 7 percentage points over the runner-up on both datasets

    Minimum-risk sequence alignment for the alignment and recognition of action videos

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    University of Technology Sydney. Faculty of Engineering and Information Technology.Temporal alignment of videos is an important requirement of tasks such as video comparison, analysis and classification. In the context of action analysis and action recognition, the main guiding element for the temporal alignment are the human actions depicted in the videos. While well-established alignment algorithms such as dynamic time warping are available, they still heavily rely on basic linear cost models and heuristic parameter tuning. Inspired by the success of the hidden Markov support vector machine for pairwise alignment of protein sequences, in this thesis we present a novel framework which combines the flexibility of a pair hidden Markov model (PHMM) with the effective parameter training of the structural support vector machine (SSVM). The framework extends the scoring function of SSVM to capture the similarity of two input frame sequences and introduces suitable feature and loss functions. During learning, we leverage these loss functions for regularised empirical risk minimisation and effective parameter selection. We have carried out extensive experiments with the proposed technique (nicknamed as EHMM-SSVM) against state-of-the-art algorithms such as dynamic time warping (DTW) and generalized canonical time warping (GCTW) on pairs of human actions from four well-known datasets. The results show that the proposed model has been able to outperform the compared algorithms by a large margin in terms of alignment accuracy. In the second part of this thesis we employ our alignment approach to tackle the task of human action recognition in video. This task is highly challenging due to the substantial variations in motion performance, recording settings and inter-personal differences. Most current research focuses on the extraction of effective features and the design of suitable classifiers. Conversely, in this thesis we tackle this problem by a dissimilarity-based approach where classification is performed in terms of minimum distance from templates and where the distance is based on the score of our alignment model, the EHMM-SSVM. In turn, the templates are chosen by means of prototype selection techniques from the available samples of each class. Experimental results over two popular human action datasets have showed that the proposed approach has been capable of achieving an accuracy higher than many existing methods and comparable to a state-of-the-art action classification algorithm

    Continuous Action Recognition Based on Sequence Alignment

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    Continuous action recognition is more challenging than isolated recognition because classification and segmentation must be simultaneously carried out. We build on the well known dynamic time warping (DTW) framework and devise a novel visual alignment technique, namely dynamic frame warping (DFW), which performs isolated recognition based on per-frame representation of videos, and on aligning a test sequence with a model sequence. Moreover, we propose two extensions which enable to perform recognition concomitant with segmentation, namely one-pass DFW and two-pass DFW. These two methods have their roots in the domain of continuous recognition of speech and, to the best of our knowledge, their extension to continuous visual action recognition has been overlooked. We test and illustrate the proposed techniques with a recently released dataset (RAVEL) and with two public-domain datasets widely used in action recognition (Hollywood-1 and Hollywood-2). We also compare the performances of the proposed isolated and continuous recognition algorithms with several recently published methods

    Minimum-risk temporal alignment of videos

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    © 2017, Springer Science+Business Media, LLC. Temporal alignment of videos is an important requirement of tasks such as video comparison, analysis and classification. Most of the approaches proposed to date for video alignment leverage dynamic programming algorithms whose parameters are manually tuned. Conversely, this paper proposes a model that can learn its parameters automatically by minimizing a meaningful loss function over a given training set of videos and alignments. For learning, we exploit the effective framework of structural SVM and we extend it with an original scoring function that suitably scores the alignment of two given videos, and a loss function that quantifies the accuracy of a predicted alignment. The experimental results from four video action datasets show that the proposed model has been able to outperform a baseline and a state-of-the-art algorithm by a large margin in terms of alignment accuracy

    Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

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    Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

    An original framework for understanding human actions and body language by using deep neural networks

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    The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

    Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome

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    The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.Comment: Accepted article in July, 2015 in Pattern Analysis and Applications, Springer. The article contains 23 pages, 4 figures, 8 tables and 51 reference
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