Browsing by Author "Liang, Yu-Ming"

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A language modeling approach to atomic human action recognition
(2007)
Liang, Yu-Ming
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Shih, Sheng-Wen
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Shih, Arthur Chun-Chieh
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Liao, Hong-Yuan Mark
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Lin, Cheng-Chung
Visual analysis of human behavior has generated considerable interest in the field of computer vision because it has a wide spectrum of potential applications. Atomic human action recognition is an important part of a human behavior analysis system. In this paper, we propose a language modeling framework for this task. The framework is comprised of two modules: a posture labeling module, and an atomic action learning and recognition module. A posture template selection algorithm is developed based on a modified shape context matching technique. The posture templates form a codebook that is used to convert input posture sequences into training symbol sequences or recognition symbol sequences. Finally, a variable-length Markov model technique is applied to learn and recognize the input symbol sequences of atomic actions. Experiments on real data demonstrate the efficacy of the proposed system.
Human Object Inpainting Using Manifold Learning-Based Posture Sequence Estimation
(2011-11-01)
Ling, Chih-Hung
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Liang, Yu-Ming
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Lin, Chia-Wen
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Chen, Yong-Sheng
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Liao, Hong-Yuan Mark
We propose a human object inpainting scheme that divides the process into three steps: 1) human posture synthesis; 2) graphical model construction; and 3) posture sequence estimation. Human posture synthesis is used to enrich the number of postures in the database, after which all the postures are used to build a graphical model that can estimate the motion tendency of an object. We also introduce two constraints to confine the motion continuity property. The first constraint limits the maximum search distance if a trajectory in the graphical model is discontinuous, and the second confines the search direction in order to maintain the tendency of an object's motion. We perform both forward and backward predictions to derive local optimal solutions. Then, to compute an overall best solution, we apply the Markov random field model and take the potential trajectory with the maximum total probability as the final result. The proposed posture sequence estimation model can help identify a set of suitable postures from the posture database to restore damaged/missing postures. It can also make a reconstructed motion sequence look continuous.
Learning Atomic Human Actions Using Variable-Length Markov Models
(2009-02-01)
Liang, Yu-Ming
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Shih, Sheng-Wen
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Shih, Arthur Chun-Chieh
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Liao, Hong-Yuan Mark
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Lin, Cheng-Chung
Visual analysis of human behavior has generated considerable interest in the field of computer vision because of its wide spectrum of potential applications. Human behavior can be segmented into atomic actions, each of which indicates a basic and complete movement. Learning and recognizing atomic human actions are essential to human behavior analysis. In this paper, we propose a framework for handling this task using variable-length Markov models (VLMMs). The framework is comprised of the following two modules: a posture labeling module and a VLMM atomic action learning and recognition module. First, a posture template selection algorithm, based on a modified shape context matching technique, is developed. The selected posture templates form a codebook that is used to convert input posture sequences into discrete symbol sequences for subsequent processing. Then, the VLMM technique is applied to learn the training symbol sequences of atomic actions. Finally, the constructed VLMMs are transformed into hidden Markov models (HMMs) for recognizing input atomic actions. This approach combines the advantages of the excellent learning function of a VLMM and the fault-tolerant recognition ability of an HMM. Experiments on realistic data demonstrate the efficacy of the proposed system.
LINEAR PRODUCTION GAME SOLUTION TO A PTZ CAMERA NETWORK
(2010)
Lai, Yu-Chun
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Liang, Yu-Ming
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Shih, Sheng-Wen
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Liao, Hong-Yuan Mark
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Lin, Cheng-Chung
Reconfiguring the PTZ parameters of a camera network is an combinatorial optimization problem and computing the optimal solution is very time consuming. Therefore, existing methods can only provide sub-optimal solutions. In this paper, a nonlinear objective function for better utilizing the cameras to track multiple targets is proposed. Furthermore, it is shown that by expanding the unknown parameters and imposing new constraints, the nonlinear objective function can be converted into a linear production game (LPG) problem. Since an LPG possesses an optimal solution which can be evaluated with polynomial time, the proposed method is efficient and accurate. Computer simulations have been conducted and the results show that the proposed method is very promising.
Linear Production Game Solution to a PTZ Camera Network
(2013-05-01)
Lai, Yu-Chun
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Liang, Yu-Ming
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Shih, Sheng-Wen
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Lin, Cheng-Chung
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Liao, Hong-Yuan Mark
Reconfiguring the parameters of PTZ cameras in a surveillance system is a combinatorial optimization problem. Computing the optimal solution is very time consuming, and existing methods can only provide sub-optimal solutions. In this paper, we propose a non-linear objective function that better utilizes a camera network to track multiple targets. We also show that, by expanding the unknown parameters and imposing new constraints, the non-linear objective function can be converted into a linear production game (LPG) problem. Since an LPG yields an optimal solution that can be evaluated in polynomial time, the proposed method is efficient and accurate. The results of simulations and a real-world experiment demonstrate the proposed method's potential.
Understanding human behavior using a language modeling approach
(2006)
Liang, Yu-Ming
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Shih, Sheng-Wen
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Shih, Arthur Chun-Chieh
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Liao, Hong-Yuan Mark
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資訊工程學系
Visual analysis of human behavior has generated considerable interest in the field of computer vision because of the wide spectrum of potential applications. In this paper, we present a language modeling framework for understanding human behavior. The proposed framework consists of two modules: the key posture selection module, and the variable-length Markov model (VLMM) behavior recognition module. A key posture selection algorithm is developed based on the shape context matching technique. A codebook is then constructed with the computed key postures and used to convert input image sequences into training symbol sequences or recognition symbol sequences. Finally, a VLMM is applied to learn and recognize the constructed symbol sequences corresponding to human behavior patterns. Experiments on real data demonstrate the efficacy of the proposed system.
Unsupervised Analysis of Human Behavior Based on Manifold Learning
(2009)
Liang, Yu-Ming
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Shih, Sheng-Wen
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Shih, Arthur Chun-Chieh
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Liao, Hong-Yuan Mark
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Lin, Cheng-Chung
In this paper, we propose a framework for unsupervised analysis of human behavior based on manifold learning. First, a pairwise human posture distance matrix is calculated from a training action sequence. Then, the isometric feature mapping (Isomap) algorithm is applied to construct a low-dimensional structure from the distance matrix. The data points in the Isomap space are consequently represented as a time-series of low-dimensional points. A temporal segmentation technique is then applied to segment the time series into subseries corresponding to atomic actions. Next, a dynamic time warping (DTW) approach is applied for clustering atomic action sequences. Finally, we use the clustering results to learn and classify atomic actions using the nearest neighbor rule. Experiments conducted on real data demonstrate the efficacy of the proposed method.
VIDEO OBJECT INPAINTING USING MANIFOLD-BASED ACTION PREDICTION
(2010)
Ling, Chih-Hung
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Liang, Yu-Ming
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Lin, Chia-Wen
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Chen, Yong-Sheng
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Liao, Hong-Yuan Mark
This paper presents a novel scheme for object completion in a video. The framework includes three steps: posture synthesis, graphical model construction, and action prediction. In the very beginning, a posture synthesis method is adopted to enrich the number of postures. Then, all postures are used to build a graphical model of object action which can provide possible motion tendency. We define two constraints to confine the motion continuity property. With the two constraints, possible candidates between every two consecutive postures are significantly reduced. Finally, we apply the Markov Random Field model to perform global matching. The proposed approach can effectively maintain the temporal continuity of the reconstructed motion. The advantage of this action prediction strategy is that it can handle the cases such as non-periodic motion or complete occlusion.