A Video Content Classification Algorithm Applying to Human Action Recognition
نویسندگان
چکیده
منابع مشابه
Supervised Statistical . . . Human Action Recognition in Video
This thesis addresses the problem of human action recognition in realistic video data,such as movies and online videos. Automatic and accurate recognition of human actionsin video is a fascinating capability. The potential applications range from surveillanceand robotics to medical diagnosis, content-based video retrieval, and intelligent human-computer interfaces. The task is h...
متن کاملHuman Action Recognition Using Spatio-temporal Classification
In this paper a framework “Temporal-Vector Trajectory Learning” (TVTL) for human action recognition is proposed. In this framework, the major concept is that we would like to add the temporal information into the action recognition process. Base on this purpose, there are three kinds of temporal information, LTM, DTM, and TTM, being proposed. With the three kinds of proposed temporal informatio...
متن کاملVideo Affective Content Recognition Based on Genetic Algorithm Combined HMM
Video affective content analysis is a fascinating but seldom addressed field in entertainment computing research communities. To recognize affective content in video, a video affective content representation and recognition framework based on Video Affective Tree (VAT) and Hidden Markov Models (HMMs) was proposed. The proposed video affective content recognizer has good potential to recognize t...
متن کاملApplying Genetic Algorithm to EEG Signals for Feature Reduction in Mental Task Classification
Brain-Computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing EEG signals measured in different mental states. Therefore, choosing suitable features is demanded for a good BCI communication. In this regard, one of the points to be considered is feature vector dimensionality. We present a method of feature reduction us...
متن کاملCompressed Video Action Recognition
Training robust deep video representations has proven to be much more challenging than learning deep image representations and consequently hampered tasks like video action recognition. This is in part due to the enormous size of raw video streams, the associated amount of computation required, and the high temporal redundancy. The ‘true’ and interesting signal is often drowned in too much irre...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronics and Electrical Engineering
سال: 2013
ISSN: 2029-5731,1392-1215
DOI: 10.5755/j01.eee.19.4.4056