Partially observable group activities (POGA) occurring in confined spaces are epitomized by their limited observability
of the objects and actions involved. In many POGA scenarios, different objects are being used by human operators for
the conduct of various operations. In this paper, we describe the ontology of such as POGA in the context of In-Vehicle
Group Activity (IVGA) recognition. Initially, we describe the virtue of ontology modeling in the context of IVGA and
show how such an ontology and a priori knowledge about the classes of in-vehicle activities can be fused for inference of
human actions that consequentially leads to understanding of human activity inside the confined space of a vehicle. In
this paper, we treat the problem of “action-object” as a duality problem. We postulate a correlation between observed
human actions and the object that is being utilized within those actions, and conversely, if an object being handled is
recognized, we may be able to expect a number of actions that are likely to be performed on that object. In this study,
we use partially observable human postural sequences to recognition actions. Inspired by convolutional neural networks
(CNNs) learning capability, we present an architecture design using a new CNN model to learn “action-object”
perception from surveillance videos. In this study, we apply a sequential Deep Hidden Markov Model (DHMM) as a
post-processor to CNN to decode realized observations into recognized actions and activities. To generate the needed
imagery data set for the training and testing of these new methods, we use the IRIS virtual simulation software to
generate high-fidelity and dynamic animated scenarios that depict in-vehicle group activities under different operational
contexts. The results of our comparative investigation are discussed and presented in detail.
Amir Shirkhodaie and Alex L. Chan, "Joint object and action recognition via fusion of partially observable surveillance imagery data," Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 1020014 (Presented at SPIE Defense + Security: April 12, 2017; Published: 2 May 2017); https://doi.org/10.1117/12.2266224.
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