Recognition of moving light displays using hidden Markov models

Biologically-Inspired Computational Neural Mechanism for Human Action/activity Recognition: A Review

“…Kinetic-geometric models also used for modification of the methods involving "what" and "where" pathways and in general models with two separated pathways and improved form pathway (answer to "what"). Spatiotemporal filters often used to replace the MLD ( [15,16,38,39,[100][101][102][103][104][105][106]) and expanded into 3D structural methods ( [1,51]), even new deep learning approaches involving 3D recognition of human action (i.e., [99]). Table 1 summarizes the pros and cons of action recognition approaches.…”

“…Figure 2 is a great representation of complex-cells implemented by ABM in form pathway [38]. The combination of spatiotemporal filter and hidden Markov model (HMM) technique was presented for MLD identification and provides decision based on the spatiotemporal sequence of the observed object features, and relatively little spatial information is caused by the segmentation of MLD image sequences, along with object identification; such information is highly temporal and is accessed by the HMM system, a major high classification rate [39] (was similar to [40]). An investigation on the spatiotemporal generalization of biological movement perception revealed the response of motion stimuli and interpolated this generalization among natural biological motion patterns [41].…”

Monocular Perception of Biological Motion in Johansson Displays

“…We choose not to address the issue of detecting and classifying pictorial features that are associated to the body parts-for the time being this has been sufficiently explored by [12,20,25,26]. Therefore our experimental setup is identical to Johansson's experiments [6,10,14,17]: we suppose that a number of markers are attached to the body of an actor. At every frame we need to attach labels to the observed features (some may be caused by noise; some body parts may have been missed).…”

Hidden Markov model-based real-time transient identifications in nuclear power plants

“…The HMM can be used to solve classification problems associated with time series input data such as speech signals or plant process signals, and can provide appropriate solutions by its modeling and learning capabilities, even though it does not have the exact knowledge to solve the problems. Most of the HMM applications for pattern classification in dynamic processes have a typical architecture to solve spatial-temporal problems, but the target systems are different, as in dynamic obstacle avoidance of mobile robot navigation, 17 radar target, 18 human action, 19 American sign language, 20 heart signals, 21 sonar signals, 22 two-handed actions, 23 conditions of an electrical machine, 24 deep space network antennae, 25 moving light displays, 26 environmental noise, 27 and human genes in DNA. 28 But the HMM has never been applied for transient identifications in NPPs.…”