Physical environment virtualization for human activities recognition

Poshtkar, Azin; Elangovan, Vinayak; Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

doi:10.1117/12.2178547

Cited by 4 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Virtual human, also referred to as avatar or agent [1], are the computer generated 3D digital representation of a human driven by a human user or a computer program. However, the creation of an interactive and responsive virtual character in real-time applications is a complex and time consuming task.…”

Section: Related Workmentioning

confidence: 99%

“…We call the category of group activities involved with such distinctive partial observability as Partially Observable Group Activities (POGA). In our previous paper [1], we introduced the concept of IVGA as a subset of the POGA. To the best of our knowledge, the problem of IVGA has not been addressed by any previous work and it is the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

In-vehicle group activity modeling and simulation in sensor-based virtual environment

Shirkhodaie

Telagamsetti

Poshtyar

et al. 2016

Signal Processing, Sensor/Information Fusion, and Target Recognition XXV

Self Cite

View full text Add to dashboard Cite

Human group activity recognition is a very complex and challenging task, especially for Partially Observable Group Activities (POGA) that occur in confined spaces with limited visual observability and often under severe occultation. In this paper, we present IRIS Virtual Environment Simulation Model (VESM) for the modeling and simulation of dynamic POGA. More specifically, we address sensor-based modeling and simulation of a specific category of POGA, called InVehicle Group Activities (IVGA). In VESM, human-alike animated characters, called humanoids, are employed to simulate complex in-vehicle group activities within the confined space of a modeled vehicle. Each articulated humanoid is kinematically modeled with comparable physical attributes and appearances that are linkable to its human counterpart. Each humanoid exhibits harmonious full-body motion -simulating human-like gestures and postures, facial impressions, and hands motions for coordinated dexterity. VESM facilitates the creation of interactive scenarios consisting of multiple humanoids with different personalities and intentions, which are capable of performing complicated human activities within the confined space inside a typical vehicle. In this paper, we demonstrate the efficiency and effectiveness of VESM in terms of its capabilities to seamlessly generate time-synchronized, multi-source, and correlated imagery datasets of IVGA, which are useful for the training and testing of multi-source full-motion video processing and annotation. Furthermore, we demonstrate full-motion video processing of such simulated scenarios under different operational contextual constraints.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-vehicle group activity modeling and simulation in sensor-based virtual environment

Shirkhodaie

Telagamsetti

Poshtyar

et al. 2016

Signal Processing, Sensor/Information Fusion, and Target Recognition XXV

Self Cite

View full text Add to dashboard Cite

show abstract

“…We call the category of group activities involved with such distinctive partial observability as Partially Observable Group Activities (POGA). In our previous paper [1], we introduced the concept of IVGA as a subset of the POGA. To the best of our knowledge, the problem of hand gesture recognition for IVGA analysis is novel and has not been addressed comprehensively by any previous work.…”

Section: Introductionmentioning

confidence: 99%

Hand gesture recognition in confined spaces with partial observability and occultation constraints

Shirkhodaie

Chan

2016

Signal Processing, Sensor/Information Fusion, and Target Recognition XXV

Self Cite

View full text Add to dashboard Cite

Human activity detection and recognition capabilities have broad applications for military and homeland security. These tasks are very complicated, however, especially when multiple persons are performing concurrent activities in confined spaces that impose significant obstruction, occultation, and observability uncertainty. In this paper, our primary contribution is to present a dedicated taxonomy and kinematic ontology that are developed for in-vehicle group human activities (IVGA). Secondly, we describe a set of hand-observable patterns that represents certain IVGA examples. Thirdly, we propose two classifiers for hand gesture recognition and compare their performance individually and jointly. Finally, we present a variant of Hidden Markov Model for Bayesian tracking, recognition, and annotation of hand motions, which enables spatiotemporal inference to human group activity perception and understanding. To validate our approach, synthetic (graphical data from virtual environment) and real physical environment video imagery are employed to verify the performance of these hand gesture classifiers, while measuring their efficiency and effectiveness based on the proposed Hidden Markov Model for tracking and interpreting dynamic spatiotemporal IVGA scenarios.

show abstract