Human Intent Prediction Using Markov Decision Processes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97080/1/AIAA2012-2445.pd

Multicamera Action Recognition with Canonical Correlation Analysis and Discriminative Sequence Classification

Proceedings of: 4th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2011, La Palma, Canary Islands, Spain, May 30 - June 3, 2011.This paper presents a feature fusion approach to the recognition of human actions from multiple cameras that avoids the computation of the 3D visual hull. Action descriptors are extracted for each one of the camera views available and projected into a common subspace that maximizes the correlation between each one of the components of the projections. That common subspace is learned using Probabilistic Canonical Correlation Analysis. The action classification is made in that subspace using a discriminative classifier. Results of the proposed method are shown for the classification of the IXMAS dataset.Publicad

A First Order Predicate Logic Formulation of the 3D Reconstruction Problem and its Solution Space

This paper defines the 3D reconstruction problem as the process of reconstructing a 3D scene from numerous 2D visual images of that scene. It is well known that this problem is ill-posed, and numerous constraints and assumptions are used in 3D reconstruction algorithms in order to reduce the solution space. Unfortunately, most constraints only work in a certain range of situations and often constraints are built into the most fundamental methods (e.g. Area Based Matching assumes that all the pixels in the window belong to the same object). This paper presents a novel formulation of the 3D reconstruction problem, using a voxel framework and first order logic equations, which does not contain any additional constraints or assumptions. Solving this formulation for a set of input images gives all the possible solutions for that set, rather than picking a solution that is deemed most likely. Using this formulation, this paper studies the problem of uniqueness in 3D reconstruction and how the solution space changes for different configurations of input images. It is found that it is not possible to guarantee a unique solution, no matter how many images are taken of the scene, their orientation or even how much color variation is in the scene itself. Results of using the formulation to reconstruct a few small voxel spaces are also presented. They show that the number of solutions is extremely large for even very small voxel spaces (5 x 5 voxel space gives 10 to 10(7) solutions). This shows the need for constraints to reduce the solution space to a reasonable size. Finally, it is noted that because of the discrete nature of the formulation, the solution space size can be easily calculated, making the formulation a useful tool to numerically evaluate the usefulness of any constraints that are added

Towards estimating computer users' mood from interaction behaviour with keyboard and mouse

The purpose of this exploratory research was to study the relationship between the mood of computer users and their use of keyboard and mouse to examine the possibility of creating a generic or individualized mood measure. To examine this, a field study (n = 26) and a controlled study (n = 16) were conducted. In the field study, interaction data and self-reported mood measurements were collected during normal PC use over several days. In the controlled study, participants worked on a programming task while listening to high or low arousing background music. Besides subjective mood measurement, galvanic skin response (GSR) data was also collected. Results found no generic relationship between the interaction data and the mood data. However, the results of the studies found significant average correlations between mood measurement and personalized regression models based on keyboard and mouse interaction data. Together the results suggest that individualized mood prediction is possible from interaction behaviour with keyboard and mouse

An Efficient Human Activity Recognition Technique Based on Deep Learning

In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to create binary space-time maps (BSTMs) which summarize human activity within a defined time interval. Finally, we use convolutional neural network (CNN) to extract features from BSTMs and classify the activities. To evaluate our approach, we carried out several tests using three public datasets: Weizmann, Keck Gesture and KTH Database. Experimental results show that our technique outperforms conventional state-of-the-art methods in term of recognition accuracy and provides comparable performance against recent deep learning techniques. It’s simple to implement, requires less computing power, and can be used for multi-subject activity recognition

Loss of ATRX in Chondrocytes Has Minimal Effects on Skeletal Development

BACKGROUND:Mutations in the human ATRX gene cause developmental defects, including skeletal deformities and dwarfism. ATRX encodes a chromatin remodeling protein, however the role of ATRX in skeletal development is currently unknown. METHODOLOGY/PRINCIPAL FINDINGS:We induced Atrx deletion in mouse cartilage using the Cre-loxP system, with Cre expression driven by the collagen II (Col2a1) promoter. Growth rate, body size and weight, and long bone length did not differ in Atrx(Col2cre) mice compared to control littermates. Histological analyses of the growth plate did not reveal any differences between control and mutant mice. Expression patterns of Sox9, a transcription factor required for cartilage morphogenesis, and p57, a marker of cell cycle arrest and hypertrophic chondrocyte differentiation, was unaffected. However, loss of ATRX in cartilage led to a delay in the ossification of the hips in some mice. We also observed hindlimb polydactily in one out of 61 mutants. CONCLUSIONS/SIGNIFICANCE:These findings indicate that ATRX is not directly required for development or growth of cartilage in the mouse, suggesting that the short stature in ATR-X patients is caused by defects in cartilage-extrinsic mechanisms

Multimodal Detection of Engagement in Groups of Children Using Rank Learning

In collaborative play, children exhibit different levels of engagement. Some children are engaged with other children while some play alone. In this study, we investigated multimodal detection of individual levels of engagement using a ranking method and non-verbal features: turn-taking and body movement. Firstly, we automatically extracted turn-taking and body movement features in naturalistic and challenging settings. Secondly, we used an ordinal annotation scheme and employed a ranking method considering the great heterogeneity and temporal dynamics of engagement that exist in interactions. We showed that levels of engagement can be characterised by relative levels between children. In particular, a ranking method, Ranking SVM, outperformed a conventional method, SVM classification. While either turn-taking or body movement features alone did not achieve promising results, combining the two features yielded significant error reduction, showing their complementary power

Online prediction of others’ actions: the contribution of the target object, action context and movement kinematics

Previous research investigated the contributions of target objects, situational context and movement kinematics to action prediction separately. The current study addresses how these three factors combine in the prediction of observed actions. Participants observed an actor whose movements were constrained by the situational context or not, and object-directed or not. After several steps, participants had to indicate how the action would continue. Experiment 1 shows that predictions were most accurate when the action was constrained and object-directed. Experiments 2A and 2B investigated whether these predictions relied more on the presence of a target object or cues in the actor’s movement kinematics. The target object was artificially moved to another location or occluded. Results suggest a crucial role for kinematics. In sum, observers predict actions based on target objects and situational constraints, and they exploit subtle movement cues of the observed actor rather than the direct visual information about target objects and context

MACI - a new era?

Full thickness articular cartilage defects have limited regenerative potential and are a significant source of pain and loss of knee function. Numerous treatment options exist, each with their own advantages and drawbacks. The goal of this review is to provide an overview of the problem of cartilage injury, a brief description of current treatment options and outcomes, and a discussion of the current principles and technique of Matrix-induced Autologous Chondrocyte Implantation (MACI). While early results of MACI have been promising, there is currently insufficient comparative and long-term outcome data to demonstrate superiority of this technique over other methods for cartilage repair