9978380
Learning a discriminative hidden part model for human action recognition
We present a discriminative part-based approach for human action recognition from video sequences using motion features. Our model is based on the recently proposed hidden conditional random field (hCRF) for object recognition. Similar to hCRF for object recognition, we model a human action by a flexible constellation of parts conditioned on image observations. Different from object recognition, our model combines both large-scale global features and local patch features to distinguish various actions. Our experimental results show that our model is comparable to other state-of-the-art approaches in action recognition. In particular, our experimental results demonstrate that combining large-scale global features and local patch features performs significantly better than directly applying hCRF on local patches alone.
38555873
Recognizing Human Action from Videos Using Histograms of Visual Words
We propose a new method for human action recognition from video sequences using histograms of visual words. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". The major difference between our model and previous "bag-of-words" models for recognition problems in computer vision is that, a "word" in our representation corresponds to a whole frame. The advantage of this representation is that the large-scale feature of a frame is better captured, which turns out to be important for recognition actions. We demonstrate our approach on two publicly available datasets. Our results are comparable to other state-of-the-art approaches for action recognition.
1878790
Unsupervised Discovery of Action Classes
In this paper we consider the problem of describing the action being performed by human figures in still images. We will attack this problem using an unsupervised learning approach, attempting to discover the set of action classes present in a large collection of training images. These action classes will then be used to label test images. Our approach uses the coarse shape of the human figures to match pairs of images. The distance between a pair of images is computed using a linear programming relaxation technique. This is a computationally expensive process, and we employ a fast pruning method to enable its use on a large collection of images. Spectral clustering is then performed using the resulting distances. We present clustering and image labeling results on a variety of datasets.
9823069
Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation
Tree-structured models have been widely used for human pose estimation, in either 2D or 3D. While such models allow efficient learning and inference, they fail to capture additional dependencies between body parts, other than kinematic constraints between connected parts. In this paper, we consider the use of multiple tree models, rather than a single tree model for human pose estimation. Our model can alleviate the limitations of a single tree-structured model by combining information provided across different tree models. The parameters of each individual tree model are trained via standard learning algorithms in a single tree-structured model. Different tree models can be combined in a discriminative fashion by a boosting procedure. We present experimental results showing the improvement of our approaches on two different datasets. On the first dataset, we use our multiple tree framework for occlusion reasoning. On the second dataset, we combine multiple deformable trees for capturing spatial constraints between non-connected body parts.
2358468
Human Action Recognition by Semilatent Topic Models
We propose two new models for human action recognition from video sequences using topic models. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". Our models differ from previous latent topic models for visual recognition in two major aspects: first of all, the latent topics in our models directly correspond to class labels; second, some of the latent variables in previous topic models become observed in our case. Our models have several advantages over other latent topic models used in visual recognition. First of all, the training is much easier due to the decoupling of the model parameters. Second, it alleviates the issue of how to choose the appropriate number of latent topics. Third, it achieves much better performance by utilizing the information provided by the class labels in the training set. We present action classification results on five different data sets. Our results are either comparable to, or significantly better than previously published results on these data sets.
6926567
Semi-Latent Dirichlet Allocation: A Hierarchical Model for Human Action Recognition
We propose a new method for human action recognition from video sequences using latent topic models. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". The major difference between our model and previous latent topic models for recognition problems in computer vision is that, our model is trained in a "semi-supervised" way. Our model has several advantages over other similar models. First of all, the training is much easier due to the decoupling of the model parameters. Secondly, it naturally solves the problem of how to choose the appropriate number of latent topics. Thirdly, it achieves much better performance by utilizing the information provided by the class labels in the training set. We present action classification and irregularity detection results, and show improvement over previous methods.
16089092
Initial experiments of a needle-holding robot for microwave ablation therapy
A needle-holding robot for microwave ablation therapy is developed to free two hands of the surgeon, so that more effort can be concentrated on monitoring the ablation procedure, which in turn guarantees good tumor ablation effect. The overall architecture of the robot is introduced first with the explanation of the mechanism of 3D motion tracking and how to minimize damping force for the robotic hand. Two experiments were conducted, one to quantify the force required for motion tracking, the other to assess the performance of motion tracking. The results of force-measuring experiment show that the force required for rotation and radial translation motion tracking are less than 15 grams, which indicates the high sensitivity of the holding robot. The results of in-vivo animal test performed on a swine show that the holding robot is accurate enough in tracking the 3D motion of the ablation needle, which allows for stable and precise holding of the needle compared to that of freehand holding.
2281946
The Determinants of Passenger Load Factor for Chinese Airline Industry
Identification of Load Factor’s Determinants is an important issue for airline industry management. This paper discusses the influence element of Chinese Airline Industry load factor based on the historical high frequency data. We set a SVAR model for the analysis and apply a TARCH-M model to discuss its ARCH effect. Our research finds that one competitor’s current and lagged PLF may affect others, and branch and associated operation also affects PLF’s fluctuation. For the branch companies competing in the same city, the effects of the positive impact and the negative impact are just opposite. The conclusions have important implications to the whole industry.
206733202
Foreground and Shadow Detection Based on Conditional Random Field
This paper presents a conditional random field (CRF) approach to integrate spatial and temporal constraints for moving object detection and cast shadow removal in image sequences. Interactions among both detection (foreground/background/shadow) labels and observed data are unified by a probabilistic framework based on the conditional random field, where the interaction strength can be adaptively adjusted in terms of data similarity of neighboring sites. Experimental results show that the proposed approach effectively fuses contextual dependencies in video sequences and significantly improves the accuracy of object detection.
15234434
Metadata driven memory optimizations in dynamic binary translator
A dynamic binary translator offers solutions for translating and running source architecture binaries on target architecture at runtime. Regardless of its growing popularity, practical dynamic binary translators usually suffer from the limited optimizations performed when generating the translated code due to the lack of useful information available in the executable files and the requirement to conform to the binary-level compatibility. Trying to generate more efficient translated code, we propose in this paper a novel method of passing performance critical information to a dynamic binary translator through the metadata section generated during the static compilation phase. With the performance critical metadata, the dynamic binary translator is able to perform aggressive optimizations to generate higher quality code. We implemented a general and extensible framework in GCC 4.0 and IA-32® Execution Layer, and selected metadata related to memory optimizations as our target. The metadata enables IA-32 EL to perform memory optimizations such as registerization, memory ordering relaxation and address disambiguation of memory instructions. Experimental data shows an overall performance improvement of 15.03% for SPECfp2000 and 1.21% for SPECint2000. For some specific benchmarks, the performance improvement is even up to 37.09%.
18702956
A Dynamic Hidden Markov Random Field Model for Foreground and Shadow Segmentation
This paper proposes a dynamic hidden Markov random field (DHMRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified in the novel dynamic probabilistic model that combines the hidden Markov model (HMM) and the Markov random field (MRF). An efficient approximate filtering algorithm is derived for the DHMRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and edge information: Moreover, models of background, shadow, and edge information are updated adoptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences