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Multiperson activity recognition in videos is a challenging task, due to the complexity of interactions among multiple persons. In this paper, a new statistical model, named coupled observation decomposed hidden Markov model (CODHMM), is presented to model multiperson activities in videos. A human activity that involves multiple persons is analyzed in two levels: the individual level that describes...
In the present, more rand more concerns have been paid to the world energy conservation and environmental protection, therefore, the development of electric vehicles are speeding up. Electric vehicles have good environmental protection performance and can taking many kinds of energy as power the prominent characteristic, and electric vehicles are considered as green transportation for the 21st century...
This paper discusses the task of human action detection in crowded videos. First, we propose a novel mask based shape matching method for action recognition. Our method does not need human detection or segmentation, and it can be used in both clean and crowed backgrounds. Next, shape and flow based features are combined due to their complementary nature. For each action, a binary sequence is used...
In this paper, we investigate the task of human action detection in crowded videos. Different from action analysis in clean scenes, action detection in crowded environments is difficult due to the cluttered backgrounds, high densities of people and partial occlusions. This paper proposes a method for action detection based on masks. No human segmentation or tracking technique is required. To cope...
Multi-person activity recognition is a challenging task due to the complex interactions between people and the multi-dimensionality of features. This paper proposes a hierarchical and observation decomposed hidden Markov model to classify multi-person activities. In order to give detailed descriptions of people's interactions by different feature scale, states of individual persons and states of interactions...
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