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Detecting actions in untrimmed videos is an important yet challenging task. In this paper, we present the structured segment network (SSN), a novel framework which models the temporal structure of each action instance via a structured temporal pyramid. On top of the pyramid, we further introduce a decomposed discriminative model comprising two classifiers, respectively for classifying actions and...
Paddy rice is one of the main grain crops in China. Accurate evaluation of rice planted area and spatial distribution are significant for the estimation ofgrain output and cropland allocation. Meanwhile, paddy rice is a main contributor to greenhouse gas emissions and therefore its cultivation and spatial distribution have significant implications for rising temperatures and global climate change...
Deep ConvNets have been shown to be effective for the task of human pose estimation from single images. However, several challenging issues arise in the video-based case such as self-occlusion, motion blur, and uncommon poses with few or no examples in the training data. Temporal information can provide additional cues about the location of body joints and help to alleviate these issues. In this paper,...
Current action recognition methods heavily rely on trimmed videos for model training. However, it is expensive and time-consuming to acquire a large-scale trimmed video dataset. This paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models from untrimmed videos without the requirement of temporal annotations of action instances...
Crude protein and amylose content are 2 important indexes of rice quality, the aim of the study is to explore an appropriate method and model to monitor the rice quality by using hyperspectral data. We colleceted samples in 2013 and 2014 when rice entered the mature period in the east of Deqing county and aquired the hyperspectral data of 4 diffenent forms of rice, including ear of rice, paddy, rice...
Agriculture plays important role in economic and social development in China. Diverse climatic and geomorphological conditions make it difficult to monitor agriculture timely and accurately in China. In this paper, we demonstrated the application of GF-1 imager and data products in agricultural monitoring in China.
The deep two-stream architecture [23] exhibited excellent performance on video based action recognition. The most computationally expensive step in this approach comes from the calculation of optical flow which prevents it to be real-time. This paper accelerates this architecture by replacing optical flow with motion vector which can be obtained directly from compressed videos without extra calculation...
Actionness [3] was introduced to quantify the likelihood of containing a generic action instance at a specific location. Accurate and efficient estimation of actionness is important in video analysis and may benefit other relevant tasks such as action recognition and action detection. This paper presents a new deep architecture for actionness estimation, called hybrid fully convolutional network (HFCN),...
Recent studies demonstrate the effectiveness of super vector representation in a number of visual recognition tasks. One popular approach along this line is the Vector of Locally Aggregated Descriptor (VLAD) where the super vector is encoded with a codebook generated by k-means. However, the effectiveness of the codebook is often limited, due to the poor clustering solution, the high dimensionality...
Event recognition from still images is one of the most important problems for image understanding. However, compared with object recognition and scene recognition, event recognition has received much less research attention in computer vision community. This paper addresses the problem of cultural event recognition in still images and focuses on applying deep learning methods on this problem. In particular,...
This paper describes our method and attempt on track 2 at the ChaLearn Looking at People (LAP) challenge 2015. Our approach utilizes Fisher vector and iDT features for action spotting, and improve its performance from two aspects: (i) We take account of interaction labels into the training process; (ii) By visualizing our results on validation set, we find that our previous method [10] is weak in...
Event recognition from still images is of great importance for image understanding. However, compared with event recognition in videos, there are much fewer research works on event recognition in images. This paper addresses the issue of event recognition from images and proposes an effective method with deep neural networks. Specifically, we design a new architecture, called Object-Scene Convolutional...
Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features [31] and deep-learned features [24]. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained...
DC microgrid is an effective solution for integrating distributed generation and various loads, which can reduce the impact on power system caused by the penetration of renewable energy systems. In this paper, control and operation of an isolated dc micro grid which consists of PV systems, wind turbines, energy storage and DC loads is investigated. Based on autonomous DC bus voltage control which...
This paper presents a load frequency control (LFC) design using sliding mode technique in a multi-area power system inculding wind turbines. In the studied system, decentralized sliding mode load frequency controller is designed such that stability of the overall closed loop system is guaranteed. A frequency response model of multi-area power system including wind turbines is introduced. The model...
Image classifications including sub pixel analysis are often used to estimate directly the crop acreage, while ground data collected during field surveys play a secondary role. This type of crop area assessment using image classifications often leads to a biased estimation due to non-representative selection of training data and subjective a-priori knowledge. Instead regression estimator approach...
The pitch angle controller is designed for SCIG wind power system by using sliding mode control to level the output power of wind turbine generation (WTG) at the rated power. The WTG system model is established by using small signal method. Then the pitch angle control system is designed based on the sliding mode control via reaching law. At last, the simulations are given by matlab software comparing...
Many mid-level representations have been developed to replace traditional bag-of-words model (VQ+fc-means) such as sparse coding, OMP-fc with fc-SVD, and fisher vector with GMM in image domain. These approaches can be split into a dictionary learning phase and a feature encoding phase which are often closely related. In this paper, we jointly evaluate the effect of these two phases for video-based...
Images and videos are often characterized by multiple types of local descriptors such as SIFT, HOG and HOF, each of which describes certain aspects of object feature. Recognition systems benefit from fusing multiple types of these descriptors. Two widely applied fusion pipelines are descriptor concatenation and kernel average. The first one is effective when different descriptors are strongly correlated,...
Location based social networks (LBSNs) are becoming increasingly popular with the fast deployment of broadband mobile networks and the growing prevalence of versatile mobile devices. This success has attracted great interest in studying and measuring the characteristics of LBSNs, such as Facebook Places, Yelp, and Google+ Local. However, it is often prohibitive, and sometimes too costly, to obtain...
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