Robust Incremental Hidden Conditional Random Fields for Action Recognition
نویسنده
چکیده
viii Εκτεταμένη Περίληψη x
منابع مشابه
Hidden Conditional Neural Fields for Continuous Phoneme Speech Recognition
In this paper, we propose Hidden Conditional Neural Fields (HCNF) for continuous phoneme speech recognition, which are a combination of Hidden Conditional Random Fields (HCRF) and a MultiLayer Perceptron (MLP), and inherit their merits, namely, the discriminative property for sequences from HCRF and the ability to extract non-linear features from an MLP. HCNF can incorporate many types of featu...
متن کاملImproved Discriminative Model for View- Invariant Human Action Recognition
Recognizing human actions play an important role in applications like video surveillance. The recent past has witnessed an increasing research on view-invariant action recognition. Huang et al. proposed a framework based on discriminative model for human action recognition. This model uses STIP (Space – Time Interest Point) to extract motion features and view invariants. Then a discriminative m...
متن کاملActivity recognition using semi-Markov models on real world smart home datasets
Accurately recognizing human activities from sensor data recorded in a smart home setting is a challenging task. Typically, probabilistic models such as the hidden Markov model (HMM) or conditional random fields (CRF) are used to map the observed sensor data onto the hidden activity states. A weakness of these models, however, is that the type of distribution used to model state durations is fi...
متن کاملConditional Random Fields for Behavior Recognition of Autonomous Underwater Vehicles
This paper focuses on multi-robot teams working cooperatively in an underwater application. Multi-robot teams working cooperatively to perform multiple tasks simultaneously have the potential to be more robust to failure and efficient when compared to single robot solutions. One key to more effective interaction is the ability to identify the behavior of other agents. However, the underwater en...
متن کاملMinimum Classification Error Training of Hidden Conditional Random Fields for Speech and Speaker Recognition
Hidden conditional random fields (HCRFs) are derived from the theory of conditional random fields with hidden-state probabilistic framework. It directly models the conditional probability of a label sequence given observations. Compared to hidden Markov models, HCRFs provide a number of benefits in the acoustic modeling of speech signals. Prior works for training on HCRFs were accomplished with...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017