Rejection of non-meaningful activities for HMM-based activity recognition system

Liu, Xiaohui; Chua, Chin-Seng

doi:10.1016/j.imavis.2009.11.001

Cited by 4 publications

(2 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An HMM-based threshold model [16] and rejection model [17] of non-meaningful activities have been used for gesture recognition. In our system, we will recognize a certain range of motion pattern; a person moves between specific landmarks or where a person may move a minimum distance from one point to another.…”

Section: Threshold Modelmentioning

confidence: 99%

See 1 more Smart Citation

Video-Based Tracking, Learning, and Recognition Method for Multiple Moving Objects

Sakaino

2013

IEEE Trans. Circuits Syst. Video Technol.

View full text Add to dashboard Cite

This paper presents an extended MCMC method for tracking and an extended HMM method for learning/recognizing multiple moving objects in videos with jittering backgrounds. A GUI with enhanced usability is also proposed. Previous MCMC and HMM based methods are known to suffer performance impairments, degraded tracking and recognition accuracy and higher computation costs, when challenged with appearance and trajectory changes such as occlusion, interaction, and varying numbers of moving objects. This paper proposes a cost reduction method for the MCMC approach by taking moves, i.e., birth and death, out of the iteration loop of the Markov chain when different moving objects interact. For stable and robust tracking, an ellipse model with stochastic model parameters is used. Moreover, our HMM method integrates several different modules in order to cope with multiple discontinuous trajectories. The GUI proposed herein offers an auto-allocation module of symbols from images and a hand-drawing module for efficient trajectory learning and for interest trajectory addition. Experiments demonstrate the advantages of our method and GUI in tracking, learning, and recognizing spatio-temporal smooth and discontinuous trajectories.

show abstract

Section: Threshold Modelmentioning

confidence: 99%

“…It is assumed that the whole trajectory is a combination of a meaningful trajectory and a noisy trajectory. The latter is dealt with as a meaningless trajectory using the threshold approach [16], [17]. We assume that there are M meaningful models as { | = 1,2, … , }.…”

Section: Threshold Modelmentioning

confidence: 99%