Human Action Recognition Based on Dense Trajectories Analysis and Random Forest

This paper presents a human action recognition method. It analyzes the spatio-temporal grids along the dense trajectories and generates the histogram of oriented gradients （HOG） and histogram of optical flow （HOF） to describe the appearance and motion of the human object. Then, HOG combined with HOF is converted to bag-of-words （BoWs） by the vocabulary tree. Finally, it applies random forest to recognize the type of human action. In the experiments, KTH database and URADL database are tested for the performance evaluation. Comparing with the other approaches, we show that our approach has a better performance for the action videos with high inter-class and low inter-class variabilities.

Guest Editorial:Intelligent Video Surveillance and Related Technologies

Due to the increasing demand for developing a secure and smart living environment, the intelligent video surveillance technology has attracted considerable attention. Building an automatic, reliable, secure, and intelligent video surveillance system has spawned large research projects and triggered many popular research topics in several international conferences and workshops recently. This special issue of Journal of ElecWonic Science and Technology （JEST） aims to present recent advances in video surveillance systems which address the observation of people in an environment, leading to a real-time description of their actions and interactions.

Real-Time Hand Motion Parameter Estimation with Feature Point Detection Using Kinect

This paper presents a real-time Kinect- based hand pose estimation method. Different from model-based and appearance-based approaches, our approach retrieves continuous hand motion parameters in real time. First, the hand region is segmented from the depth image. Then, some specific feature points on the hand are located by the random forest classifier, and the relative displacements of these feature points are transformed to a rotation invariant feature vector. Finally, the system retrieves the hand joint parameters by applying the regression functions on the feature vectors. Experimental results are compared with the ground truth dataset obtained by a data glove to show the effectiveness of our approach. The effects of different distances and different rotation angles for the estimation accuracy are also evaluated.

Quantification of Cranial Asymmetry in Infants by Facial Feature Extraction

In this paper, a facial feature extracting method is proposed to transform three-dimension （3D） head images of infants with deformational plagiocephaly for assessment of asymmetry. The features of 3D point clouds of an infant＇s cranium can be identified by local feature analysis and a two-phase k-means classification algorithm. The 3D images of infants with asymmetric cranium can then be aligned to the same pose. The mirrored head model obtained from the symmetry plane is compared with the original model for the measurement of asymmetry. Numerical data of the cranial volume can be reviewed by a pediatrician to adjust the treatment plan. The system can also be used to demonstrate the treatment progress.

Real-Time Human Body Motion Capturing System

This paper proposes a human body motion capturing system using the depth images. It consists of three processes to estimate the human pose parameters. First, we develop a pixel-based body part classifier to segment the human silhouette into different body part sub-regions and extract the primary joints. Second, we convert the distribution of the joints to the feature vector and apply the regression forest to estimate human pose parameters. Third, we apply the temporal constraints mechanism to find the best human pose parameter with the minimum estimation error. In experiments, we show that our system can operate in real-time with sufficient accuracy.

Handheld 3D Scanner for Small Objects Using Kinect

This paper presents a handheld 3D vision-based scanner for small objects by using Kinect. It is different from the previous color-glove-based approaches which require segmenting the target object. First, we eliminate the noises and the outliers caused by holding hands. Second, we apply Kinect-fusion algorithm and truncated signed distance function （TSDF） to represent 3D surfaces. Third, we propose a modified integration strategy to eliminate the hand effect. Fourth, we take advantage of the parallel computation of GPUs for real-time operation. The major contributions of this paper are （1） the registration precision is improved, （2） the oflline amendment and loop closure operation are not required, and （3） concave 3D object reconstruction is feasible.