Depth Motion Research Articles

Enhanced Depth Motion Maps for Improved Human Action Recognition from Depth Action Sequences

Enhanced Depth Motion Maps for Improved Human Action Recognition from Depth Action Sequences

Continuous psychophysics shows millisecond-scale visual processing delays are faithfully preserved in movement dynamics.

Image differences between the eyes can cause interocular discrepancies in the speed of visual processing. Millisecond-scale differences in visual processing speed can cause dramatic misperceptions of the depth and three-dimensional direction of moving objects. Here, we develop a monocular and binocular continuous target-tracking psychophysics paradigm that can quantify such tiny differences in visual processing speed. Human observers continuously tracked a target undergoing Brownian motion with a range of luminance levels in each eye. Suitable analyses recover the time course of the visuomotor response in each condition, the dependence of visual processing speed on luminance level, and the temporal evolution of processing differences between the eyes. Importantly, using a direct within-observer comparison, we show that continuous target-tracking and traditional forced-choice psychophysical methods provide estimates of interocular delays that agree on average to within a fraction of a millisecond. Thus, visual processing delays are preserved in the movement dynamics of the hand. Finally, we show analytically, and partially confirm experimentally, that differences between the temporal impulse response functions in the two eyes predict how lateral target motion causes misperceptions of motion in depth and associated tracking responses. Because continuous target tracking can accurately recover millisecond-scale differences in visual processing speed and has multiple advantages over traditional psychophysics, it should facilitate the study of temporal processing in the future.

Analysis of Wave-Induced Forces on a Floating Rectangular Box with Analytical and Numerical Approaches

A three-dimensional mathematical hydrodynamic model associated with surface wave radiation by a floating rectangular box-type structure due to heave, sway, and roll motions in finite water depth is investigated based on small amplitude water wave theory and linear structural response. The analytical expressions for the radiation potentials, wave forces, and hydrodynamic coefficients are presented based on matched eigenfunction expansion method (MEFEM). The correctness of the analytical results of wave forces is compared with the construction of a numerical model using the open-source boundary element method code NEMOH. In addition, the present result is compared with the existing published experimental results available in the literature. The effects of the different design parameters on the floating box-type rectangular structure are studied by analyzing the vertical wave force, horizontal wave force, torque, added mass, and damping coefficients due to the heave, sway, and roll motions, and the comparison analysis between the forces is also analyzed in detail. Further, the effect of reflection and transmission coefficients by varying the structural width and drafts are analyzed.

Spatio-Temporal Information Fusion and Filtration for Human Action Recognition

Human action recognition (HAR) as the most representative human-centred computer vision task is critical in human resource management (HRM), especially in human resource recruitment, performance appraisal, and employee training. Currently, prevailing approaches to human action recognition primarily emphasize either temporal or spatial features while overlooking the intricate interplay between these two dimensions. This oversight leads to less precise and robust action classification within complex human resource recruitment environments. In this paper, we propose a novel human action recognition methodology for human resource recruitment environments, which aims at symmetrically harnessing temporal and spatial information to enhance the performance of human action recognition. Specifically, we compute Depth Motion Maps (DMM) and Depth Temporal Maps (DTM) from depth video sequences as space and time descriptors, respectively. Subsequently, a novel feature fusion technique named Center Boundary Collaborative Canonical Correlation Analysis (CBCCCA) is designed to enhance the fusion of space and time features by collaboratively learning the center and boundary information of feature class space. We then introduce a spatio-temporal information filtration module to remove redundant information introduced by spatio-temporal fusion and retain discriminative details. Finally, a Support Vector Machine (SVM) is employed for human action recognition. Extensive experiments demonstrate that the proposed method has the ability to significantly improve human action recognition performance.

Residual Neural Networks for Human Action Recognition from RGB-D Videos

Recently, the RGB-D based Human Action Recognition (HAR) has gained significant research attention due to the provision of complimentary information by different data modalities. However, the current models have experienced still unsatisfactory results due to several problems including noises and view point variations between different actions. To sort out these problems, this paper proposes two new action descriptors namely Modified Depth Motion Map (MDMM) and Spherical Redundant Joint Descriptor (SRJD). MDMM eliminates the noises from depth maps and preserves only the action related information. Further SRJD ensures resilience against view point variations and reduces the misclassifications between different actions with similar view properties. Further, to maximize the recognition accuracy, standard deep learning algorithm called as Residual Neural Network (ResNet) is used to train the system through the features extracted from MDMM and SRJD. Simulation experiments prove that the multiple data modalities are better than single data modality. The proposed approach was tested on two public datasets namely NTURGB+D dataset and UTD-MHAD dataset. The testing results declare that the proposed approach is superior to the earlier HAR methods. On an average, the proposed system gained an accuracy of 90.0442% and 92.3850% at Cross-subject and Cross-view validations respectively.

Effect of Video Camera Angle on the Detection of Compensatory Movements during Motion Observation.

When exercise instructions are provided over the Internet, such as in online personal training, an instructor checks the user's form by watching their motion video recorded using a single camera device. However, fixed shooting angles may affect the detection of incorrect forms, including compensatory movements. This study aimed to verify whether differences in the shooting direction could influence compensatory movement detection by conducting motion observation using training motion videos shot from two angles. Videos of four training movements, including compensatory movements, were simultaneously captured from the front and side. Ten university students studying physical therapy watched the videos from each angle to detect compensatory movements. This study revealed significant differences between the plane of motion in which the compensatory action occurred and the direction of shooting for the false responses in the compensatory action detection for the three movements (p < 0.05). The results indicated that the shooting direction and the plane of motion in which the compensatory action occurred affected the detection of compensatory movements, which was attributable to differences in information on the amount of joint change depending on the direction of joint motion observation and to a lack of binocular visual information necessary for depth motion detection.

Perceiving depth and motion in depth from successive occlusion.

Occlusion, or interposition, is one of the strongest and best-known pictorial cues to depth. Furthermore, the successive occlusions of previous objects by newly presented objects produces an impression of increasing depth. Although the perceived motion associated with this illusion has been studied, the depth percept has not. To investigate, participants were presented with two piles of disks with one always static and the other either a static pile or a stacking pile where a new disk was added every 200ms. We found static piles with equal number of disks appeared equal in height. In contrast, the successive presentation of disks in the stacking condition appeared to enhance the perceived height of the stack-fewer disks were needed to match the static pile. Surprisingly, participants were also more precise when comparing stacking versus static piles of disks. Reversing the stacking by removing rather than adding disks reversed the bias and degraded precision. In follow-up experiments, we used nonoverlapping static and dynamic configurations to show that the effects are not due to simple differences in perceived numerosity. In sum, our results show that successive occlusions generate a greater sense of height than occlusion alone, and we posit that dynamic occlusion may be an underappreciated source of depth information.

Research on hot isostatic pressing sintering behavior of 90W–Ni–Fe–Cu alloy

In order to understand the underlying material evolution during liquid phase sintering (LPS) process in hot isostatic pressing process (HIP), microstructure and mechanical properties of 90 W–Ni–Fe–Cu alloys fabricated at 1400 °C/150 MPa are examined systematically. For 90W-4.2Ni-1.8Fe–4Cu alloy, the typical LPS microstructure, which are spherical W grains uniformly surrounded by continuously distributed γ- (Ni, Fe, Cu) matrix phase with the lowest W–W contiguity of 0.31, is achieved successfully after HIP at 1400 °C/150 MPa. The excellent tensile strength reaches as high as 873 MPa with elongation of 17.3% due to the soft γ- (Ni, Fe, Cu) matrix phase. Moreover, the relationship between the microstructure parameters VM(1−CW−W) and elongation ε are established and analyze the association with dislocation motion in depth through electron backscattered diffraction (EBSD) characterization. To our best knowledge, the LPS behavior of tungsten alloy is in-situ revealed by high-temperature confocal scanning laser microscopy (HT-CSLM) for the first time. The current research provides a novel strategy for designing and fabricating powder metallurgy materials by LPS with pressure.

Multimodal construction of human movement dataset under the development of physical dance teaching in colleges and universities

Abstract First, this paper proposes a multimodal fusion-based human motion recognition technique applied to college physical dance teaching, where feature-level or decision-level fusion maps human kinematic semantic information from datasets to classifiers under a single-level stage. Secondly, a multi-level multimodal fusion approach for human motion recognition is proposed to make it more adaptable to practical application scenarios. The depth data is converted into a depth motion projection map at the input side, and the inertial data is converted into a signal image. Finally, all the modalities are trained by a convolutional neural network for extracting features, and the extracted features are fused at the feature level by discriminant correlation analysis. The results show that the multi-level multimodal fusion framework achieves 99.8% and 99.9% recognition accuracy on the two datasets, respectively, with 100% accuracy in the Throw and Catch action recognition and the lowest recognition rate of 95.36% in the Clap action test, with an average recognition rate of 97.89%, which has a high recognition accuracy. The multi-level multimodal fusion model can obtain movement data close to the actual movement data after optimization in low-precision movement data, which provides data support for physical dance teaching and learning.

The effect of real-world and retinal motion on speed perception for motion in depth.

For motion in depth, even if the target moves at a constant speed in the real-world (physically), it would appear to be moving with acceleration on the retina. Therefore, the purpose of this study was to determine whether real-world and retinal motion affect speed perception in depth and to verify the influence of eye movements on both motion signals in judging speed in depth. We used a two-alternative forced-choice paradigm with two types of tasks. One stimulus moved at a constant speed in the real-world (world constant task) with three conditions: 80-60 cm (far), 60-40 cm (middle), and 40-20 cm (near) from the participant. The other stimulus moved at a constant speed on the retina (retinal constant task) with three conditions: 4-8 deg (far), 8-12 deg (middle), and 12-16 deg (near) as the vergence angle. The results showed that stimulus speed was perceived faster in the near condition than in the middle and far conditions for the world constant task, regardless of whether it was during fixation or convergence eye movements. In contrast, stimulus speed was perceived faster in the order of the far, middle, and near conditions for the retinal constant task. Our results indicate that speed perception of a visual target approaching the observer depends on real-world motion when the target position is relatively far from the observer. In contrast, retinal motion may influence speed perception when the target position is close to the observer. Our results also indicate that the effects of real-world and retinal motion on speed perception for motion in depth are similar with or without convergence eye movements. Therefore, it is suggested that when the visual target moves from far to near, the effects of real-world and retinal motion on speed perception are different depending on the initial target position.

Research on the Control Performance of Depth-Fixed Motion of Underwater Vehicle Based on Fuzzy-PID

The control performance of the fixed-depth motion is related to the performance of the underwater vehicle. Due to the complex and changing environment underwater and various potential risks, the variety of underwater operations, and the variability of the structural parameters and environmental parameters of the underwater vehicle, the control performance is compromised when performing constant depth motion. It is of much significance to study the control method of the fixed-depth motion to improve the performance of the underwater vehicle. The transfer function of underwater vehicle’s depth-setting motion was established, and the fuzzy-PID controller was established for simulating the control of depth-setting movements of underwater vehicles. The interaction law between PID initial parameters and controller performance was studied, and the interaction law between the change of underwater vehicle mass and the hydrodynamic coefficient and controller performance was also studied. The results show that the fuzzy-PID controller can realize the control of the underwater vehicle’s depth-setting motion, and the control effect was independent of the initial PID parameters, thus avoiding the dependence of the formulation of PID parameters on manual control experience. When the mass and hydrodynamic coefficient of the underwater vehicle change, the fuzzy-PID controller can still maintain good control performance and has strong adaptive ability.

Contribution of the stereoscopic representation of motion-in-depth during visually guided feedback control.

Considerable studies have focused on the neural basis of visually guided tracking movement in the frontoparallel plane, whereas the neural process in real-world circumstances regarding the influence of binocular disparity and motion-in-depth (MID) perception is less understood. Although the role of stereoscopic versus monoscopic MID information has been extensively described for visual processing, its influence on top-down regulation for motor execution has not received much attention. Here, we orthogonally varied the visual representation (stereoscopic versus monoscopic) and motion direction (depth motion versus bias depth motion versus frontoparallel motion) during visually guided tracking movements, with simultaneous functional near-infrared spectroscopy recordings. Results show that the stereoscopic representation of MID could lead to more accurate movements, which was supported by specific neural activity pattern. More importantly, we extend prior evidence about the role of frontoparietal network in brain-behavior relationship, showing that occipital area, more specifically, visual area V2/V3 was also robustly involved in the association. Furthermore, by using the stereoscopic representation of MID, it is plausible to detect robust brain-behavior relationship even with small sample size at low executive task demand. Taken together, these findings highlight the importance of the stereoscopic representation of MID for investigating neural correlates of visually guided feedback control.

Multimodal spatial-temporal feature representation and its application in action recognition

目的 在人体行为识别研究中，利用多模态方法将深度数据与骨骼数据相融合，可有效提高动作的识别率。针对深度图像信息数据量大、冗余度高等问题，提出一种通过获取关键时程信息动作帧序列降低冗余的算法，即质心运动路径松弛算法，并根据不同模态数据的特点，提出一种新的时空特征表示方法。方法 质心运动路径松弛算法根据质心在相邻帧之间的运动距离，计算图像差分后获得的活跃部分的相似系数，然后剔除掉相似度高的帧，获得足以表达行为的关键时程信息。根据图像动态部分的变化特性、人体各部分在运动中的协同性和局部显著性特征构建一种新的时空特征表示方法。结果 在MSR-Action3D数据集上对本文方法的效果进行验证。在3个子集中进行交叉验证的平均分类识别率为95.743 2%，分别比Multi-fused，CovP3DJ，D3D-LSTM（densely connected 3DCNN and long short-term memory），Joint Subset Selection方法高2.443 2%，4.763 2%，0.343 2%，0.213 2%。本文方法在使用完整数据集的扩展实验中进行交叉验证的分类识别率为93.040 3%，具有很好的鲁棒性。结论 实验结果表明，本文提出的去冗余算法在降低冗余后提升了识别效果，提取的特征之间具有相关性低的特点，在组合识别中具有良好的互补性，有效提高了分类识别的精确度。;Objective Human body motion-related recognition has been developing in the context of computer vision and pattern recognition like auxiliary human-computer interaction，motion analysis，intelligent monitoring，and virtual reality. To obtain two-dimensional information for its behavioral recognition，conventional motion behavior recognition is mainly used the RGB image sequence captured by RGB camera. To improve the ability to detect short-duration fragments，current feature descriptors for RGB image sequences are employed to characterize human behavior，such as histogram of oriented gradient（HOG），histogram of optical flow （HOF），and a three-dimensional feature pyramid. Some researchers are focused on the feature that image depth is insensitive to ambient light since RGB images are oriented to behavior image sequences of objects in terms of two-dimensional information. The depth information of the image is coordinated with the features of RGB image to describe the related behavior. Human behavior recognition-relevant multi-modal method can be used to fuse depth data and skeleton data，which can improve the recognition rate of action effectively. Recent depth map is widely used in relevant to human behavior recognition. But，the collection of depth information data is required to be optimized because of time complexity of feature extraction and space complexity of feature storage. To resolve the problems，we develop an algorithm to optimize frames of the depth map and resource consumption. At the same time，a new representation of motion features is facilitated as well according to the motion information of the centroid. Method First，the temporal feature vector is used in terms of depth map sequence-extracted time sequence information. The centroid motion path relaxation algorithm is used to realize depth image de-duplication and de redundancy，and the skeleton map-extracted spatial structure feature vector from are spliced to form the spatio-temporal feature input. Next，spatial features are extracted in terms of the original skeleton points coordinates-spliced three-channel spatial feature map. Finally，the fusion probability of spatio-temporal features and spatial features is used for classification and recognition. Our centroid motion path relaxation algorithm is focused on the optimization of redundant information，the time complexity of feature extraction，and the space complexity of feature storage. For the skeleton data，the global feature of motion direction is proposed to fully reflect the integrity and coordination of limb movements. The extracted features are concatenated to obtain the spatio-temporal feature vector，and they can be fused and enhanced through the original coordinates of skeleton points-built three-channel spatial feature map. Its effectiveness is verified on the MSR-Action3D dataset. Result The experimental setting 1 demonstrate that it is 0. 826 0% higher than the depth motion map（DMM）-local binary pattern（LBP）algorithm，1. 015 2% higher than DMM-CRC（collaborative representation classifier），3. 450 1% higher than gradient local auto correlation（DMM-GLAC） algorithm，0. 605 8% higher than EigenJoint algorithm，and 0. 605 8% higher than space-time auto correlation of gradient （STACOG）algorithm is 10. 624 5% higher. After removing redundancy，the result of experimental setting 1 is 0. 126 1% higher as well. The cross-validation on experimental setting 2 show that the average classification and recognition rate in the three subsets is 95. 743 2%，2. 443 2% higher than multi-fused method，4. 763 2% higher than CovP3DJ method，0. 343 2% higher than D3D-LSTM method，and 0. 213 2% higher than joint subset selection method. For the overall data set，it is 2. 030 3% higher than low latency method，0. 240 3% higher than combination of deep models method，and 2. 340 3% higher than complex network coding method. The experimental setting 2 illustrates that the average classification recognition rate of cross-validation in three subsets is 95. 743 2%，and the classification recognition rate of the complete dataset is 93. 040 3%. Conclusion Our algorithm proposed can improve the recognition effect based on redundancy-optimized，and the featuresextracted have lower correlation mutually，which can improve the accuracy of classification recognition effectively.

Time-delay high-order sliding mode control for trajectory tracking of autonomous underwater vehicles under disturbances

This paper presents a new generalized super-twisting algorithm (GSTA) controller with a time-delay estimator (TDE) for the tracking control of an autonomous underwater vehicle (AUV) under disturbances. For exploration tasks in confined areas, AUVs are typically equipped with sensors such as Doppler velocity logs (DVL) or acoustic modems to measure their position and speed. However, these sensors have a disadvantage in that their acquisition rates are low. This disadvantage, combined with the influence of external disturbances, directly influences position control because it requires partial knowledge of the system for proper operation. To solve this problem, the introduction of a TDE that estimates the vehicle hydrodynamics with delayed sensor information, and consequently, improves the controller performance is proposed. For controller validation, a stability analysis is presented, and two results are provided in the simulation. In the first test, the AUV is controlled to track a parameterized spiral-shaped path to validate the vehicle movement in the three directions of the plane (x,y,z). In the second test, the AUV performs path tracking in yaw and depth motions. In all numerical simulations, three types of disturbances/uncertainties are considered: (i) system parameters, (ii) sensor readings, and (iii) control.

Introduction to Brownian Motion in Class XII Senior High School 5 Bengkulu City

Brownian motion is the random motion of nanoparticles in the fluid. The concept of Brownian motion is one of the abstract concepts of physics. Thus, it is difficult to learn. Most schools also don't teach Brownian motion in depth in high school. Including the Senior High School 5 Bengkulu City. In fact, there are a lot of Brown motion applications. For this reason, the introduction of Brownian motion in easy-to-understand language needs to be taught to students. The method used in this research is the method of preparation, implementation, and evaluation. From this research, it can be seen that students are very enthusiastic about listening and answering every question that is asked. There is also a very active participation. The competitive spirit of students is also visible. So, it can be concluded that, no matter how difficult and abstract Brown's motion is, if it is taught in simple language, students are also able to understand it well.

Spatial and temporal information fusion for human action recognition via Center Boundary Balancing Multimodal Classifier

This paper proposes a novel multimodal data classifier named Center Boundary Balancing Multimodal Classifier (CBBMC) to fuse and classify the spatial and temporal descriptors for recognizing human actions from depth video sequences. CBBMC is a composite algorithm integrating feature fusion and feature classification, in which Center Boundary Balancing Projection (CBBP) is used to balance the center and boundary information of feature class spaces. In order to solve the problem of multimodal information redundancy and isolation, two feature selection and fusion schemes of CBBMC based on embedded feature selection are presented. Moreover, two new action descriptors called Gaussian Pyramid Depth Motion Images (GP-DMI) and Depth Temporal Maps (DTM) are introduced to capture the multi-scale spatial and fine-grained temporal information of human activities. Finally, we present an effective spatial and temporal information fusion framework based on CBBMC for human action recognition. In order to evaluate the performance of the proposed approach, extensive experiments are conducted. The proposed method achieved impressive results on four benchmark datasets, namely MSR Action3D (96.33%), UTD-MHAD (94.41%), DHA (95.65%), and NTU RGB+D (83.31% cross-subject and 87.66% cross-view), even though only the depth modality was used. The experimental results demonstrate the effectiveness of our method.

Reliability of 3D Depth Motion Sensors for Capturing Upper Body Motions and Assessing the Quality of Wheelchair Transfers.

Wheelchair users must use proper technique when performing sitting-pivot-transfers (SPTs) to prevent upper extremity pain and discomfort. Current methods to analyze the quality of SPTs include the TransKinect, a combination of machine learning (ML) models, and the Transfer Assessment Instrument (TAI), to automatically score the quality of a transfer using Microsoft Kinect V2. With the discontinuation of the V2, there is a necessity to determine the compatibility of other commercial sensors. The Intel RealSense D435 and the Microsoft Kinect Azure were compared against the V2 for inter- and intra-sensor reliability. A secondary analysis with the Azure was also performed to analyze its performance with the existing ML models used to predict transfer quality. The intra- and inter-sensor reliability was higher for the Azure and V2 (n = 7; ICC = 0.63 to 0.92) than the RealSense and V2 (n = 30; ICC = 0.13 to 0.7) for four key features. Additionally, the V2 and the Azure both showed high agreement with each other on the ML outcomes but not against a ground truth. Therefore, the ML models may need to be retrained ideally with the Azure, as it was found to be a more reliable and robust sensor for tracking wheelchair transfers in comparison to the V2.

Analysis of Badminton Movement Cognition Algorithm Based on Track Linear Capture

At present, more and more sports science and technology are being explored and applied in competitive sports. The birth and popularization of video tracking and capturing technology have provided more fair and just perspectives for many sports events. Track linear capture can replay the player’s behavior in real time, the flight path of the badminton can be analyzed in 3D stereoscopic analysis, and the ball’s motion trajectory can be calculated more accurately. In this paper, an objective trajectory tracking and prediction model is constructed based on the motion cognition algorithm, and the motion characteristics of the objective are extracted from the limited historical trajectory of the objective to achieve more accurate trajectory tracking. Then, the trajectory tracking model is applied to the objective tracking framework to obtain ideal objective tracking results. At the same time, in order to make use of the interaction between scene information and objective, this paper improves the trajectory tracking model. The trajectory prediction model based on neural network is constructed, which learns the pedestrian motion characteristics from the pedestrian trajectory data of the target tracking scene offline and uses its “memory” online to generate the implicit depth motion characteristics of the target according to the limited historical information of the target. It also predicts the most likely location of the future target and calculates the motion similarity between the targets. Finally, a simulation experiment platform is built to prove the effectiveness of the trajectory tracking model and objective tracking algorithm proposed in this paper. Through the research results of this paper, it can play a role in verifying the referee’s judgment on the penalty of some key balls, which is more conducive to maintaining the fairness of the game, and more helpful for athletes to optimize their exercise results according to scientific basis, and has the function of improving their performance.

Implementation Details for Controlling Contactless 3D Virtual Endoscopy

In the medical world, with the innovative application of medical informatics, it is possible to enable many aspects of surgeries that were not able to be addressed before. One of these is contactless surgery planning and controlling the visualization of medical data. In our approach to contactless surgery, we adopted a new framework for hand and motion detection based on augmented reality. We developed a contactless interface for a surgeon to control the visualization options in our DICOM (Digital Imaging and Communications in Medicine) viewer platform that uses a stereo camera as a sensor device input that controls hand/finger motions, in contactless mode, and applied it to 3D virtual endoscopy. In this paper, we will present our proposal for defining motion parameters in contactless, incisionless surgeries. We enabled better surgeon’s experience, more precise surgery, real-time feedback, depth motion tracking, and contactless control of visualization, which gives freedom to the surgeon during the surgery. We implemented motion tracking using stereo cameras with depth resolution and precise shutter sensors for depth streaming. Our solution provides contactless control with a range up to 2–3 m that enables the application in the operating room.

Using a Selective Ensemble Support Vector Machine to Fuse Multimodal Features for Human Action Recognition.

The traditional human action recognition (HAR) method is based on RGB video. Recently, with the introduction of Microsoft Kinect and other consumer class depth cameras, HAR based on RGB-D (RGB-Depth) has drawn increasing attention from scholars and industry. Compared with the traditional method, the HAR based on RGB-D has high accuracy and strong robustness. In this paper, using a selective ensemble support vector machine to fuse multimodal features for human action recognition is proposed. The algorithm combines the improved HOG feature-based RGB modal data, the depth motion map-based local binary pattern features (DMM-LBP), and the hybrid joint features (HJF)-based joints modal data. Concomitantly, a frame-based selective ensemble support vector machine classification model (SESVM) is proposed, which effectively integrates the selective ensemble strategy with the selection of SVM base classifiers, thus increasing the differences between the base classifiers. The experimental results have demonstrated that the proposed method is simple, fast, and efficient on public datasets in comparison with other action recognition algorithms.