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Continuous action recognition in video is more complicated compared with traditional isolated action recognition. Besides the high variability of postures and appearances of each action, the complex temporal dynamics of continuous action makes this problem challenging. In this study, the authors propose a hierarchical framework combining convolutional neural network (CNN) and hidden Markov model (HMM),...
Continuous action recognition in video is more challenging compared with traditional isolated action recognition. In this paper, we proposed a hybrid framework combining Convolutional Neural Network (CNN) and Latent-Dynamic Conditional Random Field (LDCRF) to segment and recognize continuous actions simultaneously. Most existing action recognition works construct complex handcrafted features, which...
In this work, we investigate to recognize house numbers captured in street view images. We formulate the problem as sequence recognition and present an integrated model by combining Convolutional Neural Network (CNN) and Hidden Markov Model (HMM). Our method utilizes representation capability of CNN to model the highly variable appearance of digits. Meanwhile, HMM is used to handle the dynamics of...
In this paper, we analyze the importance and the difficulties to recognize car plate characters from low-quality monitoring videos. A solution is proposed to achieve the character recognition by classifying character images based on convolutional neural network. We analyze the degradation of car plate character in practical surveillance videos, and then model and generated simulated dataset. We also...
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