In this paper we present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. In particular, we propose the use of computer vision and social signal processing technologies in real scenarios of Victim-Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real world Victim-Offender Mediation sessions in Catalonia in collaboration with the regional government. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state-of-the-art binary classification approaches, our system achieves recognition accuracies of 86 when predicting satisfaction, and 79 when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1-5] for the computed social signals.
The Restorative Justice approach focuses on the personal needs of victims. As discussed above, achieving success in the VOM sessions depends largely on how the participants communicate with each other. A large number of techniques can be found in the literature for application in VOM. A good example of this is provided by Umbreit's handbook @cite_20 . This resource offers an empirically grounded, state-of-the-art analysis of the application and impact of VOM. It provides practical guidance and resources for VOM in the case of property crimes, minor assaults, and, more recently, crimes of severe violence, where family members of murder victims request a meeting with the offender. Since most of these cases addressed are of a highly sensitive nature, participants are likely to manifest emotional states, when interacting with the others, that can be physically observed through their non-verbal communication @cite_31 .
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Foreword by Marlene Young. Introduction: Restorative Justice Through Victim Offender Mediation. PHILOSOPHY, PRACTICE, AND CONTEXT. Humanistic Mediation: A Transformative Journey of Peacemaking. Guidelines for Victim-Sensitive Mediation and Dialogue with Offenders. The Mediation Process: Phases and Tasks. Multicultural Implications of Victim Offender Mediation. Case Studies. National Survey of Victim Offender Mediation Programs. Program Development Issues. WHAT WE ARE LEARNING FROM RESEARCH. The Impact of Victim Offender Mediation: Two Decades of Research. Cross-National Assessment of Victim Offender Mediation. Victim Offender Mediation in the United States: A Multisite Assessment. Victim Offender Mediation in Canada: A Multisite Assessment. Victim Offender Mediation in England: A Multisite Assessment. EMERGING ISSUES. Advanced Mediation and Dialogue in Crimes of Severe Violence. Potential Hazards and Opportunities. Appendix A. Resources: Organizations, Publications, Videotapes. Appendix B. Directory of VOM Programs in the United States. Appendix C. Program Profiles. Appendix D. Promising Practices and Innovations. Appendix E. Summary of Forty VOM Empirical Studies. Appendix F. Assessing Participant Satisfaction with VOM. References. Index. In this paper, we focus on improving the proposal classification stage in the object detection task and present implicit negative sub-categorization and sink diversion to lift the performance by strengthening loss function in this stage. First, based on the observation that the “background” class is generally very diverse and thus challenging to be handled as a single indiscriminative class in existing state-of-the-art methods, we propose to divide the background category into multiple implicit sub-categories to explicitly differentiate diverse patterns within it. Second, since the ground truth class inevitably has low-value probability scores for certain images, we propose to add a “sink” class and divert the probabilities of wrong classes to this class when necessary, such that the ground truth label will still have a higher probability than other wrong classes even though it has low probability output. Additionally, we propose to use dilated convolution, which is widely used in the semantic segmentation task, for efficient and valuable context information extraction. Extensive experiments on PASCAL VOC 2007 and 2012 data sets show that our proposed methods based on faster R-CNN implementation can achieve state-of-the-art mAPs, i.e., 84.1 , 82.6 , respectively, and obtain 2.5 improvement on ILSVRC DET compared with that of ResNet. We present a self-supervised approach to ignoring "distractors" in camera images for the purposes of robustly estimating vehicle motion in cluttered urban environments. We leverage offline multi-session mapping approaches to automatically generate a per-pixel ephemerality mask and depth map for each input image, which we use to train a deep convolutional network. At run-time we use the predicted ephemerality and depth as an input to a monocular visual odometry (VO) pipeline, using either sparse features or dense photometric matching. Our approach yields metric-scale VO using only a single camera and can recover the correct egomotion even when 90 of the image is obscured by dynamic, independently moving objects. We evaluate our robust VO methods on more than 400km of driving from the Oxford RobotCar Dataset and demonstrate reduced odometry drift and significantly improved egomotion estimation in the presence of large moving vehicles in urban traffic. 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.