5 research outputs found
Analyzing Human-Human Interactions: A Survey
Many videos depict people, and it is their interactions that inform us of
their activities, relation to one another and the cultural and social setting.
With advances in human action recognition, researchers have begun to address
the automated recognition of these human-human interactions from video. The
main challenges stem from dealing with the considerable variation in recording
setting, the appearance of the people depicted and the coordinated performance
of their interaction. This survey provides a summary of these challenges and
datasets to address these, followed by an in-depth discussion of relevant
vision-based recognition and detection methods. We focus on recent, promising
work based on deep learning and convolutional neural networks (CNNs). Finally,
we outline directions to overcome the limitations of the current
state-of-the-art to analyze and, eventually, understand social human actions
Spatio-Temporal Detection of Fine-Grained Dyadic Human Interactions
We introduce a novel spatio-temporal deformable part model for offline detection of fine-grained interactions in video. One novelty of the model is that part detectors model the interacting individuals in a single graph that can contain different combinations of feature descriptors. This allows us to use both body pose and movement to model the coordination between two people in space and time. We evaluate the performance of our approach on novel and existing interaction datasets. When testing only on the target class, we achieve mean average precision scores of 0.82. When presented with distractor classes, the additional modelling of the motion of specific body parts significantly reduces the number of confusions. Cross-dataset tests demonstrate that our trained models generalize well to other settings
Spatio-Temporal Detection of Fine-Grained Dyadic Human Interactions
We introduce a novel spatio-temporal deformable part model for offline detection of fine-grained interactions in video. One novelty of the model is that part detectors model the interacting individuals in a single graph that can contain different combinations of feature descriptors. This allows us to use both body pose and movement to model the coordination between two people in space and time. We evaluate the performance of our approach on novel and existing interaction datasets. When testing only on the target class, we achieve mean average precision scores of 0.82. When presented with distractor classes, the additional modelling of the motion of specific body parts significantly reduces the number of confusions. Cross-dataset tests demonstrate that our trained models generalize well to other settings
Multimodal Data Analysis of Dyadic Interactions for an Automated Feedback System Supporting Parent Implementation of Pivotal Response Treatment
abstract: Parents fulfill a pivotal role in early childhood development of social and communication
skills. In children with autism, the development of these skills can be delayed. Applied
behavioral analysis (ABA) techniques have been created to aid in skill acquisition.
Among these, pivotal response treatment (PRT) has been empirically shown to foster
improvements. Research into PRT implementation has also shown that parents can be
trained to be effective interventionists for their children. The current difficulty in PRT
training is how to disseminate training to parents who need it, and how to support and
motivate practitioners after training.
Evaluation of the parents’ fidelity to implementation is often undertaken using video
probes that depict the dyadic interaction occurring between the parent and the child during
PRT sessions. These videos are time consuming for clinicians to process, and often result
in only minimal feedback for the parents. Current trends in technology could be utilized to
alleviate the manual cost of extracting data from the videos, affording greater
opportunities for providing clinician created feedback as well as automated assessments.
The naturalistic context of the video probes along with the dependence on ubiquitous
recording devices creates a difficult scenario for classification tasks. The domain of the
PRT video probes can be expected to have high levels of both aleatory and epistemic
uncertainty. Addressing these challenges requires examination of the multimodal data
along with implementation and evaluation of classification algorithms. This is explored
through the use of a new dataset of PRT videos.
The relationship between the parent and the clinician is important. The clinician can
provide support and help build self-efficacy in addition to providing knowledge and
modeling of treatment procedures. Facilitating this relationship along with automated
feedback not only provides the opportunity to present expert feedback to the parent, but
also allows the clinician to aid in personalizing the classification models. By utilizing a
human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the
classification models by providing additional labeled samples. This will allow the system
to improve classification and provides a person-centered approach to extracting
multimodal data from PRT video probes.Dissertation/ThesisDoctoral Dissertation Computer Science 201