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In this paper, a novel view invariant action recognition method based on neural network representation and recognition is proposed. The novel representation of action videos is based on learning spatially related human body posture prototypes using self organizing maps. Fuzzy distances from human body posture prototypes are used to produce a time invariant(More)
In this paper a novel view-invariant movement recognition method is presented. A multi-camera setup is used to capture the movement from different observation angles. Identification of the position of each camera with respect to the subject's body is achieved by a procedure based on morphological operations and the proportions of the human body. Binary body(More)
In this paper, we present a view-independent action recognition method exploiting a low computational-cost volumetric action representation. Binary images depicting the human body during action execution are accumulated in order to produce the so-called action volumes. A novel time-invariant action representation is obtained by exploiting the circular shift(More)
In this paper we present a dynamic classification scheme involving Single-hidden Layer Feedforward Neural (SLFN) network-based non-linear data mapping and test sample-specific labeled data selection in multiple levels. The number of levels is dynamically determined by the test sample under consideration, while the use of Extreme Learning Machine (ELM)(More)
In this paper, a method aiming at view-independent human action recognition is presented. Actions are described as series of successive human body poses. Action videos representation is based on fuzzy vector quantization, while action classification is performed by a novel classification algorithm, the so-called Sparsity-based Learning Machine (SbLM),(More)
In this paper, a novel view invariant person identification method based on human activity information is proposed. Unlike most methods proposed in the literature, in which “walk” (i.e., gait) is assumed to be the only activity exploited for person identification, we incorporate several activities in order to identify a person. A multicamera(More)
Eating and drinking activity recognition can be considered a solitary research field in activity recognition area. The development of an application capable to identify human eating and drinking activity can be really useful in a smart home environment targeting to extend independent living of older persons in the early stages of dementia. In this paper a(More)