Visual event recognition using decision trees

Simon, Cédric; Meessen, Jerome; Vleeschouwer, Christophe De

doi:10.1007/s11042-009-0364-y

Cited by 23 publications

(8 citation statements)

References 25 publications

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“…Whatever are the features, most of the works used a learning algorithm to learn the exacted features. HMMs [3], SVMs [4] and decision trees [5] were most popular in HAR.…”

Section: Literature Reviewmentioning

confidence: 99%

Convolutional Long Short-Term Memory Hybrid Networks for Skeletal Based Human Action Recognition

Prasad*,

Kishore,

Rao

2020

IJITEE

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The objective is to develop a time series image representation of the skeletal action data and use it for recognition through a convolutional long short-term deep learning framework. Consequently, Kinect captured human skeletal data is transformed into a Joint Change Distance Image (JCDI) descriptor which maps the time changes in the joints. Subsequently, JCDIs are decoded spatially well with a Convolutional (CNN). Temporal decomposition is executed on long short term memory (LSTM) with data changes along x , y and z position vectors of the skeleton. We propose a combination of CNN and LSTM which maps the spatio temporal information to generate a generalized time series features for recognition. Finally, scores are fused from spatially vibrant CNNs and temporally sound LSTMs for action recognition. Publicly available action datasets such as NTU RGBD, MSR Action, UTKinect and G3D were used as test inputs for experimentation. The results showed a better performance due to spatio temporal modeling at both the representation and the recognition stages when compared to other state-of-the-arts

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“…Whatever are the features, most of the works used a learning algorithm to learn the exacted features. HMMs [3], SVMs [4] and decision trees [5] were most popular in HAR.…”

Section: Literature Reviewmentioning

confidence: 99%

Convolutional Long Short-Term Memory Hybrid Networks for Skeletal Based Human Action Recognition

Prasad*,

Kishore,

Rao

2020

IJITEE

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“…Common models are HMMs [12], Bayesian networks [6], context free grammars [18], and other graphical models [16]. Methods focusing on global behavior understand- ing often rely on object tracking [21,6,18].…”

Section: Previous Workmentioning

confidence: 99%

Meta-tracking for video scene understanding

Jodoin

Benezeth²,

Wang

2013

2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance

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“…Common models are HMMs [34,38,41,55,62], Bayesian networks [12,59], context free grammars [56]. and other graphical model [36,49,57]. As for the classification stage, it is either used to recognize pre-defined patterns of activity [8,10,16,23,28,47,49,60,61] (useful for counting [16.52]) or detect anomalies by flagging everything that deviates from what has been previously learned [5,14,25,39,41,42,45].…”

Section: Related Workmentioning

confidence: 99%

Smart Distributed Sensor Fields: Algorithms for Tactical Sensors

Saligrama¹

2013

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Visual event recognition using decision trees

Cited by 23 publications

References 25 publications

Convolutional Long Short-Term Memory Hybrid Networks for Skeletal Based Human Action Recognition

Convolutional Long Short-Term Memory Hybrid Networks for Skeletal Based Human Action Recognition

Meta-tracking for video scene understanding

Smart Distributed Sensor Fields: Algorithms for Tactical Sensors

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