Human Action Recognition Using Improved Salient Dense Trajectories
نویسندگان
چکیده
منابع مشابه
Human Action Recognition Using Improved Salient Dense Trajectories
Human action recognition in videos is a topic of active research in computer vision. Dense trajectory (DT) features were shown to be efficient for representing videos in state-of-the-art approaches. In this paper, we present a more effective approach of video representation using improved salient dense trajectories: first, detecting the motion salient region and extracting the dense trajectorie...
متن کاملHybrid Super Vector with Improved Dense Trajectories for Action Recognition
With recent improved dense trajectory features (HOG, warped HOF, and warped MBH), we employ two advanced super vector methods, namely Fisher Vector (FV) and soft Vector of Locally Aggregated Descriptors (VLAD-K) to encode them separately. The two individual super vectors are concatenated into a Hybrid Super Vector, and a linear SVM classifier is used to predict labels. We achieve 87.46%1 in ave...
متن کاملVision-based action recognition of construction workers using dense trajectories
Wide spread monitoring cameras on construction sites provide large amount of information for construction management. The emerging of computer vision and machine learning technologies enables automated recognition of construction activities from videos. As the executors of construction, the activities of construction workers have strong impact on productivity and progress. Compared to machine w...
متن کاملLearning features from Improved Dense Trajectories using deep convolutional networks for Human Activity Recognition
In this work, we tackle the problem of recognizing human activities by exploring methods for incorporating the state of the art improved dense trajectories into a deep learning framework. Specifically, we explore efficacy of several models trained using the action tubes sampled from dense trajectory. We performed experiments two different architectures, the first one that resembles bag of words...
متن کاملAction Detection with Improved Dense Trajectories and Sliding Window
In this paper we describe an action/interaction detection system based on improved dense trajectories [20], multiple visual descriptors and bag-of-features representation. Given that the actions/interactions are not mutual exclusive, we train a binary classifier for every predefined action/interaction. We rely on a non-overlapped temporal sliding window to enable the temporal localization. We h...
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: Computational Intelligence and Neuroscience
سال: 2016
ISSN: 1687-5265,1687-5273
DOI: 10.1155/2016/6750459