nils-herrmann/clean_scirepeval huggingface dataset:
Structure and appearance features for robust 3D facial actions tracking

This paper presents a robust and accurate method for joint head pose and facial actions tracking, even under challenging conditions such as varying lighting, large head movements, and fast motion. This is made possible by the combination of two types of facial features. We use locations sampled from the facial texture whose appearance is initialized on the first frame and adapted over time, and also illumination-invariant patches located on characteristic points of the face such as the corners of the eyes or of the mouth. The first type of features contains rich information about the global appearance of the face and thus leads to an accurate tracking, while the second type guaranties robustness and stability by avoiding drift. We demonstrate our system on the Boston University Face Tracking benchmark, and show it outperforms state-of-the-art methods.
Multimedia Analysis and Access of Ancient Maya Epigraphy: Tools to support scholars on Maya hieroglyphics

This article presents an integrated framework for multimedia access and analysis of ancient Maya epigraphic resources, which is developed as an interdisciplinary effort involving epigraphers (someone who deciphers ancient inscriptions) and computer scientists. Our work includes several contributions: a definition of consistent conventions to generate high-quality representations of Maya hieroglyphs from the three most valuable ancient codices, which currently reside in European museums and institutions; a digital repository system for glyph annotation and management; as well as automatic glyph retrieval and classification methods. We study the combination of statistical Maya language models and shape representation within a hieroglyph retrieval system, the impact of applying language models extracted from different hieroglyphic resources on various data types, and the effect of shape representation choices for glyph classification. A novel Maya hieroglyph data set is given, which can be used for shape analysis benchmarks, and also to study the ancient Maya writing system.
Sports Event Recognition Using Layered HMMS

The recognition of events in video data is a subject of much current interest. In this paper, we address several issues related to this topic. The first one is overfitting when very large feature spaces are used and relatively small amounts of training data are available. The second is the use of a framework that can recognise events at different time scales, as standard hidden Markov model (HMM) do not model well long-term term temporal dependencies in the data. In this paper we propose a method combining layered HMMs and an unsupervised low level clustering of the features to address these issues. Experiments conducted on the recognition task of different events in 7 rugby games demonstrates the potential of our approach with respect to standard HMM techniques coupled with a feature size reduction technique. While the current focus of this work is on events in sports videos, we believe the techniques shown here are general enough to be applied to other sources of data
Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition

This paper describes a novel method called Deep Dynamic Neural Networks (DDNN) for multimodal gesture recognition. A semi-supervised hierarchical dynamic framework based on a Hidden Markov Model (HMM) is proposed for simultaneous gesture segmentation and recognition where skeleton joint information, depth and RGB images, are the multimodal input observations. Unlike most traditional approaches that rely on the construction of complex handcrafted features, our approach learns high-level spatiotemporal representations using deep neural networks suited to the input modality: a Gaussian-Bernouilli Deep Belief Network (DBN) to handle skeletal dynamics, and a 3D Convolutional Neural Network (3DCNN) to manage and fuse batches of depth and RGB images. This is achieved through the modeling and learning of the emission probabilities of the HMM required to infer the gesture sequence. This purely data driven approach achieves a Jaccard index score of 0.81 in the ChaLearn LAP gesture spotting challenge. The performance is on par with a variety of state-of-the-art hand-tuned feature-based approaches and other learning-based methods, therefore opening the door to the use of deep learning techniques in order to further explore multimodal time series data.
Multi-party focus of attention recognition in meetings from head pose and multimodal contextual cues

This paper presents investigations on visual focus of attention (VFOA) recognition in meetings from audio-visual perceptual cues. Rather than independently recognizing the VFOA of each participant from his own head pose, we propose to recognize participants' VFOA jointly in order to introduce context dependent interaction models that relates to group activity and the social dynamics of communication. To this end, we designed an input-output hidden Markov model (IOHMM), whose hidden states are the joint VFOA of all participants, and whose main observations are the head poses. Interaction models are introduced in the form of contextual cues that affect the temporal evolution of the joint VFOA sequence, allowing us to model group dynamics that accounts for people's tendency to share the same focus, or to have their VFOA driven by contextual cues such as slide activity or the participant speaking activity. The model is rigorously evaluated on a publicly available dataset of 4 real meetings of 23min on average, showing an overall 10% relative performance increase w.r.t. the independent recognition case.
Tracking the Visual Focus of Attention for a Varying Number of Wandering People

In this paper, we define and address the problem of finding the visual focus of attention for a varying number of wandering people (VFOA-W), determining where a person is looking when their movement is unconstrained. The VFOA-W estimation is a new and important problem with implications in behavior understanding and cognitive science and real-world applications. One such application, presented in this paper, monitors the attention passers-by pay to an outdoor advertisement by using a single video camera. In our approach to the VFOA-W problem, we propose a multiperson tracking solution based on a dynamic Bayesian network that simultaneously infers the number of people in a scene, their body locations, their head locations, and their head pose. For efficient inference in the resulting variable-dimensional state-space, we propose a Reversible-Jump Markov Chain Monte Carlo (RJMCMC) sampling scheme and a novel global observation model, which determines the number of people in the scene and their locations. To determine if a person is looking at the advertisement or not, we propose Gaussian Mixture Model (GMM)-based and Hidden Markov Model (HMM)-based VFOA-W models, which use head pose and location information. Our models are evaluated for tracking performance and ability to recognize people looking at an outdoor advertisement, with results indicating good performance on sequences where up to three mobile observers pass in front of an advertisement.
A Cognitive and Unsupervised Map Adaptation Approach to the Recognition of the Focus of Attention from Head Pose

In this paper, the recognition of the visual focus of attention (VFOA) of meeting participants (as defined by their eye gaze direction) from their head pose is addressed. To this end, the head pose observations are modeled using an hidden Markov model (HMM) whose hidden states corresponds to the VFOA. The novelties are threefold. First, contrary to previous studies on the topic, in our set-up, the potential VFOA of a person is not restricted to other participants only, but includes environmental targets (a table and a projection screen), which increases the complexity of the task, with more VFOA targets spread in the pan and tilt (as well) gaze space. Second, the HMM parameters are set by exploiting results from the cognitive science on saccadic eye motion, which allows to predict what the head pose should be given an actual gaze target. Third, an unsupervised parameter adaptation step is proposed which accounts for the specific gazing behaviour of each participant. Using a publicly available corpus of 8 meetings featuring 4 persons, we analyze the above methods by evaluating, through objective performance measures, the recognition of the VFOA from head pose information obtained either using a magnetic sensor device or a vision based tracking system.
Extracting and locating temporal motifs in video scenes using a hierarchical non parametric Bayesian model

In this paper, we present an unsupervised method for mining activities in videos. From unlabeled video sequences of a scene, our method can automatically recover what are the recurrent temporal activity patterns (or motifs) and when they occur. Using non parametric Bayesian methods, we are able to automatically find both the underlying number of motifs and the number of motif occurrences in each document. The model's robustness is first validated on synthetic data. It is then applied on a large set of video data from state-of-the-art papers. We show that it can effectively recover temporal activities with high semantics for humans and strong temporal information. The model is also used for prediction where it is shown to be as efficient as other approaches. Although illustrated on video sequences, this model can be directly applied to various kinds of time series where multiple activities occur simultaneously.
Parameter estimation and contextual adaptation for a multi-object tracking CRF model

We present a detection-based approach to multi-object tracking formulated as a statistical labeling task and solved using a Conditional Random Field (CRF) model. The CRF model relies on factors involving detection pairs and their corresponding hidden labels. These factors model pairwise position or color similarities as well as dissimilarities, and one critical issue is to be able to learn their parameters in an accurate and unsupervised way. We argue in this paper that tracklets and local context can help to obtain relevant parameters. In this context, the contributions are as follows: i) a factor term global parameter estimation based on intermediate tracking results; ii) a detection dependent parameter adaptation scheme that allows to take into account the local detection contextual information during online tracking. Experiments on PETS 2009 and CAVIAR datasets show the validity of our approach, and similar or better performance than recent state-of-the-art algorithms.
Person independent 3D gaze estimation from remote RGB-D cameras

We address the problem of person independent 3D gaze estimation using a remote, low resolution, RGB-D camera. The approach relies on a sparse technique to reconstruct normalized eye test images from a gaze appearance model (a set of eye image/gaze pairs) and infer their gaze accordingly. In this context, the paper makes three contributions: (i) unlike most previous approaches, we exploit the coupling (and constraints) between both eyes to infer their gaze jointly; (ii) we show that a generic gaze appearance model built from the aggregation of person-specific models can be used to handle unseen users and compensate for appearance variations across people, since a test user eyes' appearance will be reconstructed from similar users within the generic model. (iii) we propose an automatic model selection method that leads to comparable performance with a reduced computational load.
Gaze estimation from multimodal Kinect data

This paper addresses the problem of free gaze estimation under unrestricted head motion. More precisely, unlike previous approaches that mainly focus on estimating gaze towards a small planar screen, we propose a method to estimate the gaze direction in the 3D space. In this context the paper makes the following contributions: (i) leveraging on Kinect device, we propose a multimodal method that rely on depth sensing to obtain robust and accurate head pose tracking even under large head pose, and on the visual data to obtain the remaining eye-in-head gaze directional information from the eye image; (ii) a rectification scheme of the image that exploits the 3D mesh tracking, allowing to conduct a head pose free eye-in-head gaze directional estimation; (iii) a simple way of collecting ground truth data thanks to the Kinect device. Results on three users demonstrate the great potential of our approach.
Given that, should i respond? Contextual addressee estimation in multi-party human-robot interactions

In this paper, we investigate the task of addressee estimation in multi-party interactions. For every utterance from a human participant, the robot should know if it was being addressed or not, so as to respond and behave accordingly. To accomplish this various cues could be made use of: the most important being gaze cues of the speaker. Apart from this several other cues can act as contextual variables to improve the estimation accuracy of this task. For example, the gaze cue of other participants, and the long-term or short-term dialog context. In this paper we investigate the possibility to combine such information from diverse sources to improve the addressee estimation task. For this study, we use 11 interactions with a humanoid robot NAO1 giving quiz to two human participants.
Geometric Generative Gaze Estimation (G3E) for Remote RGB-D Cameras

We propose a head pose invariant gaze estimation model for distant RGB-D cameras. It relies on a geometric understanding of the 3D gaze action and generation of eye images. By introducing a semantic segmentation of the eye region within a generative process, the model (i) avoids the critical feature tracking of geometrical approaches requiring high resolution images, (ii) decouples the person dependent geometry from the ambient conditions, allowing adaptation to different conditions without retraining. Priors in the generative framework are adequate for training from few samples. In addition, the model is capable of gaze extrapolation allowing for less restrictive training schemes. Comparisons with state of the art methods validate these properties which make our method highly valuable for addressing many diverse tasks in sociology, HRI and HCI.
Maya Codical Glyph Segmentation: A Crowdsourcing Approach

This paper focuses on the crowd-annotation of an ancient Maya glyph dataset derived from the three ancient codices that survived up to date. More precisely, nonexpert annotators are asked to segment glyph-blocks into their constituent glyph entities. As a means of supervision, available glyph variants are provided to the annotators during the crowdsourcing task. Compared to object recognition in natural images or handwriting transcription tasks, designing an engaging task and dealing with crowd behavior is challenging in our case. This challenge originates from the inherent complexity of Maya writing and an incomplete understanding of the signs and semantics in the existing catalogs. We elaborate on the evolution of the crowdsourcing task design, and discuss the choices for providing supervision during the task. We analyze the distributions of similarity and task difficulty scores, and the segmentation performance of the crowd. A unique dataset of over 9000 Maya glyphs from 291 categories individually segmented from the three codices was created and will be made publicly available thanks to this process. This dataset lends itself to automatic glyph classification tasks. We provide baseline methods for glyph classification using traditional shape descriptors and convolutional neural networks.
Using particles to track varying numbers of interacting people

In this paper, we present a Bayesian framework for the fully automatic tracking of a variable number of interacting targets using a fixed camera. This framework uses a joint multi-object state-space formulation and a trans-dimensional Markov Chain Monte Carlo (MCMC) particle filter to recursively estimates the multi-object configuration and efficiently search the state-space. We also define a global observation model comprised of color and binary measurements capable of discriminating between different numbers of objects in the scene. We present results which show that our method is capable of tracking varying numbers of people through several challenging real-world tracking situations such as full/partial occlusion and entering/leaving the scene.
A probabilistic framework for joint head tracking and pose estimation

Head tracking and pose estimation are usually considered as two sequential and separate problems: pose is estimated on the head patch provided by a tracking module. However, precision in head pose estimation is dependent on tracking accuracy which itself could benefit from the head orientation knowledge. Therefore, this work considers head tracking and pose estimation as two coupled problems in a probabilistic setting. Head pose models are learned and incorporated into a mixed-state particle filter framework for joint head tracking and pose estimation. Experimental results on real sequences show the effectiveness of the method in estimating more stable and accurate pose values.
Retrieving ancient Maya glyphs with Shape Context

We introduce an interdisciplinary project for archaeological and computer vision research teams on the analysis of the ancient Maya writing system. Our first task is the automatic retrieval of Maya syllabic glyphs using the Shape Context descriptor. We investigated the effect of several parameters to adapt the shape descriptor given the high complexity of the shapes and their diversity in our data. We propose an improvement in the cost function used to compute similarity between shapes making it more restrictive and precise. Our results are promising, they are analyzed via standard image retrieval measurements.
Bridging the past, present and future: Modeling scene activities from event relationships and global rules

This paper addresses the discovery of activities and learns the underlying processes that govern their occurrences over time in complex surveillance scenes. To this end, we propose a novel topic model that accounts for the two main factors that affect these occurrences: (1) the existence of global scene states that regulate which of the activities can spontaneously occur; (2) local rules that link past activity occurrences to current ones with temporal lags. These complementary factors are mixed in the probabilistic generative process, thanks to the use of a binary random variable that selects for each activity occurrence which one of the above two factors is applicable. All model parameters are efficiently inferred using a collapsed Gibbs sampling inference scheme. Experiments on various datasets from the literature show that the model is able to capture temporal processes at multiple scales: the scene-level first order Markovian process, and causal relationships amongst activities that can be used to predict which activity can happen after another one, and after what delay, thus providing a rich interpretation of the scene's dynamical content.
Visual focus of attention estimation from head pose posterior probability distributions

We address the problem of recognizing the visual focus of attention (VFOA) of meeting participants from their head pose and contextual cues. The main contribution of the paper is the use of a head pose posterior distribution as a representation of the head pose information contained in the image data. This posterior encodes the probabilities of the different head poses given the image data, and constitute therefore a richer representation of the data than the mean or the mode of this distribution, as done in all previous work. These observations are exploited in a joint interaction model of all meeting participants pose observations, VFOAs, speaking status and of environmental contextual cues. Numerical experiments on a public database of 4 meetings of 22 min on average show that this change of representation allows for a 5.4% gain with respect to the standard approach using head pose as observation.
Detection of multiple moving objects using multiscale MRF with camera motion compensation

We address the problem of detecting moving objects from a moving camera. The apparent flow field induced by the camera motion is modeled by a 2D parametric motion model and compensated for using the values of the parameters estimated by a multiresolution robust method. Motion detection is achieved through a statistical regularization approach based on multiscale Markov random field (MRF) models. Particular attention has been paid to the definition of the energy function involved and to the considered observations. This method has been validated by experiments carried out on different real image sequences.<<ETX>>
Audiovisual Probabilistic Tracking of Multiple Speakers in Meetings

Tracking speakers in multiparty conversations constitutes a fundamental task for automatic meeting analysis. In this paper, we present a novel probabilistic approach to jointly track the location and speaking activity of multiple speakers in a multisensor meeting room, equipped with a small microphone array and multiple uncalibrated cameras. Our framework is based on a mixed-state dynamic graphical model defined on a multiperson state-space, which includes the explicit definition of a proximity-based interaction model. The model integrates audiovisual (AV) data through a novel observation model. Audio observations are derived from a source localization algorithm. Visual observations are based on models of the shape and spatial structure of human heads. Approximate inference in our model, needed given its complexity, is performed with a Markov Chain Monte Carlo particle filter (MCMC-PF), which results in high sampling efficiency. We present results-based on an objective evaluation procedure-that show that our framework 1) is capable of locating and tracking the position and speaking activity of multiple meeting participants engaged in real conversations with good accuracy, 2) can deal with cases of visual clutter and occlusion, and 3) significantly outperforms a traditional sampling-based approach
Visual activity context for focus of attention estimation in dynamic meetings

We address the problem of recognizing, in dynamic meetings in which people do not remain seated all the time, the visual focus of attention (VFOA) of seated people from their head pose and contextual activity cues. We propose a model that comprises the VFOA of a meeting participant as the hidden state, and his head pose as the observation. To account for the presence of moving visual targets due to the dynamic nature of the meeting, the locations of the visual targets are used as an input variables to the head pose observation model. Contextual information is introduced in the VFOA dynamics through a slide activity variable and speaking or visual activity variables that relate people's focus to the meeting activity context. The main novelty of this paper is the introduction of visual activity context for FOA recognition to account for the correlation between a person's focus and the other people's gestures, hand and body motions. We evaluate our model on a large dataset of 5 hours. Our results show that, for VFOA estimation in meetings, visual activity contextual information can be as effective as speaking context.