Georgios Th. Papadopoulos

Real-Time Skeleton-Tracking-Based Human Action Recognition Using Kinect Data

In this paper, a real-time tracking-based approach to human action recognition is proposed. The method receives as input depth map data streams from a single kinect sensor. Initially, a skeleton-tracking algorithm is applied. Then, a new action representation is introduced, which is based on the calculation of spherical angles between selected joints and the respective angular velocities. For invariance incorporation, a pose estimation step is applied and all features are extracted according to a continuously updated torso-centered coordinate system; this is different from the usual practice of using common normalization operators. Additionally, the approach includes a motion energy-based methodology for applying horizontal symmetry. Finally, action recognition is realized using Hidden Markov Models (HMMs). Experimental results using the Huawei/3DLife 3D human reconstruction and action recognition Grand Challenge dataset demonstrate the efficiency of the proposed approach.

Statistical Motion Information Extraction and Representation for Semantic Video Analysis

In this paper, an approach to semantic video analysis that is based on the statistical processing and representation of the motion signal is presented. Overall, the examined video is temporally segmented into shots and for every resulting shot appropriate motion features are extracted; using these, hidden Markov models (HMMs) are employed for performing the association of each shot with one of the semantic classes that are of interest. The novel contributions of this paper lie in the areas of motion information processing and representation. Regarding the motion information processing, the kurtosis of the optical flow motion estimates is calculated for identifying which motion values originate from true motion rather than measurement noise. Additionally, unlike the majority of the approaches of the relevant literature that are mainly limited to global- or camera-level motion representations, a new representation for providing local-level motion information to HMMs is also presented. It focuses only on the pixels where true motion is observed. For the selected pixels, energy distribution-related information, as well as a complementary set of features that highlight particular spatial attributes of the motion signal, are extracted. Experimental results, as well as comparative evaluation, from the application of the proposed approach in the domains of Tennis, News and Volleyball broadcast video, and Human Action video demonstrate the efficiency of the proposed method.

Evidence-Driven Image Interpretation by Combining Implicit and Explicit Knowledge in a Bayesian Network

Computer vision techniques have made considerable progress in recognizing object categories by learning models that normally rely on a set of discriminative features. However, in contrast to human perception that makes extensive use of logic-based rules, these models fail to benefit from knowledge that is explicitly provided. In this paper, we propose a framework that can perform knowledge-assisted analysis of visual content. We use ontologies to model the domain knowledge and a set of conditional probabilities to model the application context. Then, a Bayesian network is used for integrating statistical and explicit knowledge and performing hypothesis testing using evidence-driven probabilistic inference. In addition, we propose the use of a focus-of-attention (FoA) mechanism that is based on the mutual information between concepts. This mechanism selects the most prominent hypotheses to be verified/tested by the BN, hence removing the need to exhaustively test all possible combinations of the hypotheses set. We experimentally evaluate our framework using content from three domains and for the following three tasks: 1) image categorization; 2) localized region labeling; and 3) weak annotation of video shot keyframes. The results obtained demonstrate the improvement in performance compared to a set of baseline concept classifiers that are not aware of any context or domain knowledge. Finally, we also demonstrate the ability of the proposed FoA mechanism to significantly reduce the computational cost of visual inference while obtaining results comparable to the exhaustive case.

Integrating Image Segmentation and Classification for Fuzzy Knowledge-Based Multimedia Indexing

In this paper we propose a methodology for semantic indexing of images, based on techniques of image segmentation, classification and fuzzy reasoning. The proposed knowledge-assisted analysis architecture integrates algorithms applied on three overlapping levels of semantic information: i) no semantics, i.e. segmentation based on low-level features such as color and shape, ii) mid-level semantics, such as concurrent image segmentation and object detection, region-based classification and, iii) rich semantics, i.e. fuzzy reasoning for extraction of implicit knowledge. In that way, we extract semantic description of raw multimedia content and use it for indexing and retrieval purposes, backed up by a fuzzy knowledge repository. We conducted several experiments to evaluate each technique, as well as the whole methodology in overall and, results show the potential of our approach.

Accumulated motion energy fields estimation and representation for semantic event detection

In this paper, a motion-based approach for detecting high-level semantic events in video sequences is presented. Its main characteristic is its generic nature, i.e. it can be directly applied to any possible domain of concern without the need for domain-specific algorithmic modifications or adaptations. For realizing event detection, the video is initially segmented into shots and for every resulting shot appropriate motion features are extracted at fixed time intervals, thus forming a motion observation sequence. Then, Hidden Markov Models (HMMs) are employed for associating each shot with a semantic event based on its formed observation sequence. Regarding the motion feature extraction procedure, a new representation for providing local-level motion information to HMMs is presented, while motion characteristics from previous frames are also exploited. The latter is based on the observation that motion information from previous frames can provide valuable cues for interpreting the semantics present in a particular frame. Experimental results as well as comparative evaluation from the application of the proposed approach in the domains of tennis and news broadcast video are presented.

Ontology-driven semantic video analysis using visual information objects

In this paper, an ontology-driven approach for the semantic analysis of video is proposed. This approach builds on an ontology infrastructure and in particular a multimedia ontology that is based on the notions of Visual Information Object (VIO) and Multimedia Information Object (MMIO). The latter constitute extensions of the Information Object (IO) design pattern, previously proposed for refining and extending the DOLCE core ontology. This multimedia ontology, along with the more domain-specific parts of the developed knowledge infrastructure, supports the analysis of video material, models the content layer of video, and defines generic as well as domain-specific concepts whose detection is important for the analysis and description of video of the specified domain. The signal-level video processing that is necessary for linking the developed ontology infrastructure with the signal domain includes the combined use of a temporal and a spatial segmentation algorithm, a layered structure of Support Vector Machines (SVMs)-based classifiers and a classifier fusion mechanism. A Genetic Algorithm (GA) is introduced for optimizing the performed information fusion step. These processing methods support the decomposition of visual information, as specified by the multimedia ontology, and the detection of the defined domain-specific concepts that each piece of video signal, treated as a VIO, is related to. Experimental results in the domain of disaster news video demonstrate the efficiency of the proposed approach.

A Comparative Study of Classification Techniques for Knowledge-Assisted Image Analysis

In this paper, four individual approaches to region classification for knowledge-assisted semantic image analysis are presented and comparatively evaluated. All of the examined approaches realize knowledge-assisted analysis via implicit knowledge acquisition, i.e. are based on machine learning techniques such as support vector machines (SVMs), self organizing maps (SOMs), genetic algorithm (GA)and particle swarm optimization (PSO). Under all examined approaches, each image is initially segmented and suitable low-level descriptors are extracted for every resulting segment. Then, each of the aforementioned classifiers is applied to associate every region with a predefined high-level semantic concept. An appropriate evaluation framework has been employed for the comparative evaluation of the above algorithms under varying experimental conditions.

Estimation and representation of accumulated motion characteristics for semantic event detection

In this paper, a motion-based approach for detecting high-level semantic events in video sequences is presented. Its main characteristic is its generic nature, i.e. it can be directly applied to any possible domain of concern without the need for domain-specific algorithmic modifications or adaptations. For realizing event detection, the examined video sequence is initially segmented into shots and for every resulting shot appropriate motion features are extracted. Then, Hidden Markov Models (HMMs) are employed for performing the association of each shot with one of the high-level semantic events that are of interest in any given domain. Regarding the motion feature extraction procedure, a new representation for providing local-level motion information to HMMs is presented, while motion characteristics from previous frames are also exploited. Experimental results as well as comparative evaluation from the application of the proposed approach in the domain of news broadcast video are presented.

An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding

This work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus and pattern recognition, relies also on general knowledge and application context for understanding visual content in conceptual terms. Our work is an attempt to imitate this behavior by devising an evidence driven probabilistic inference framework using ontologies and bayesian networks. Experiments conducted for two different image analysis tasks showed improvement in performance, compared to the case where computer vision techniques act isolated from any type of knowledge or context.

Human Action Recognition Using 3D Reconstruction Data

In this paper, the problem of human action recognition using 3D reconstruction data is deeply investigated. 3D reconstruction techniques are employed for addressing two of the most challenging issues related to human action recognition in the general case, namely view variance (i.e., when the same action is observed from different viewpoints) and the presence of (self-) occlusions (i.e., when for a given point of view a body part of an individual conceals another body part of the same or another subject). The main contributions of this paper are summarized as follows. The first one is a detailed examination of the use of 3D reconstruction data for performing human action recognition. The latter includes the introduction of appropriate local/global flow/shape descriptors, extensive experiments in challenging publicly available datasets and exhaustive comparisons with state-of-art approaches. The second one is a new local-level 3D flow descriptor, which incorporates spatial and surface information in the flow representation and efficiently handles the problem of defining 3D orientation at every local neighborhood. The third one is a new global-level 3D flow descriptor that efficiently encodes the global motion characteristics in a compact way. The fourth one is a novel global temporal-shape descriptor that extends the notion of 3D shape descriptions for action recognition, by incorporating the temporal dimension. The proposed descriptor efficiently addresses the inherent problems of temporal alignment and compact representation, while also being robust in the presence of noise (compared with similar tracking-based methods of the literature). Overall, this paper significantly improves the state-of-art performance and introduces new research directions in the field of 3D action recognition, following the recent development and wide-spread use of portable, affordable, high-quality and accurate motion capturing devices (e.g., Microsoft Kinect).