Md. Atiqur Rahman Ahad

Motion history image: its variants and applications

The motion history image (MHI) approach is a view-based temporal template method which is simple but robust in representing movements and is widely employed by various research groups for action recognition, motion analysis and other related applications. In this paper, we provide an overview of MHI-based human motion recognition techniques and applications. Since the inception of the MHI template for motion representation, various approaches have been adopted to improve this basic MHI technique. We present all important variants of the MHI method. This paper points some areas for further research based on the MHI method and its variants.

Human activity recognition: Various paradigms

Action and activity representation and recognition are very demanding research area in computer vision and man-machine interaction. Though plenty of researches have been done in this arena, the field is still immature. Over the last decades, extensive research methodologies have been developed on human activity analysis and recognition for various applications. This paper overviews various recent methods for human activity recognition with analysis. We attempt to sum up the various methods related to human motion representation and recognition. We make an effort to categorize the recent methods from the best in the business, and finally figure out the short-comings and challenges to dig out in future to develop robust action recognition approaches. This work exclusively endeavors to encompass the researches related only to human action recognition mainly from 2001 till-to-date with critical assessment of the methods. We also present our work along with to solve some of the shortcomings. It will widely benefit the researchers to understand and compare the related advancements in this area.

Analysis of Motion Self-Occlusion Problem Due to Motion Overwriting for Human Activity Recognition

Various recognition methodologies address to recognize and understand varieties of human activities. However, motion self-occlusion due to motion overlapping in the same region is a daunting task to solve. Various motion-recognition methods either bypass this problem or solve this problem in complex manner. Appearance-based template matching paradigms are simpler and hence these approaches faster for activity analysis. In this paper, we concentrate on motion self- occlusion problem due to motion overlapping in various complex activities for recognition. In the Motion History Image (MHI) method, the self-occlusion is evident and it should be solved. Therefore, this paper compares our directional motion history image concept with basic the Motion History Image, Multi-level Motion History representation and Hierarchical Motion History Histogram representation to solve the self-occlusion problem of basic the Motion History Image representation. We employ some complex aerobics and find the robustness of our method compared to other methods for this self-occlusion problem. We employ seven higher order Hu moments to compute the feature vector for each activity. Afterwards, k-nearest neighbor method is utilized for classification with leave-one-out paradigm. The comparative results clearly demonstrate the superiority of our method than other recent approaches. We also present several experiments to demonstrate the performance and strength of the DMHI method in recognizing various complex actions. Index Terms—MHI, DMHI, MMHI, HMHH, motion recognition, feature vector

Motion recognition approach to solve overwriting in complex actions

Motion overwriting due to motion self-occlusion is a big concern in motion and activity recognition. This paper presents a directional motion recognition approach that can solve the motion overwriting for complex actions or activities. Optical flow is split into four directions to compute motion templates. These templates are used to create feature vectors by Hu moments. Very satisfactory recognition results are achieved for various complex actions, which encompass motion overwriting. This method is compared with the basic motion history image method and multi-level motion history image method. The latter method professed that it can overcome motion self-occlusion problem and hence we compare these methods for several complex datasets with complex dimensions.

Action recognition by employing combined directional motion history and energy images

Human action understanding and analysis for various applications are still in infancy due to various factors. In this paper, for recognizing various complex activities, a combined cue for motion representation and later recognition is demonstrated based on the optical flow-based four directional motion history and basic energy images. Optical flow between consecutive frames are computed to create the update function and to segment the moving regions. These motion vectors are split into four different channels. From these channels, the corresponding four directional history templates are computed. These along with frame-subtracted energy motion templates represent the final motion information of an action sequence. From these templates, feature vectors are calculated according to the seven Hu invariants. We develop a 35-dimensional feature vector for each action. For classification, k-nearest neighbor classification scheme is employed. For partitioning scheme, we employ leave-one-out cross-validation method. Both indoor and outdoor dataset provide satisfactory recognition results. These analysis, representation can be used for robot vision, interactive systems, computer games, behavior understanding, etc.

Moment-based human motion recognition from the representation of DMHI templates

This paper presents a noble appearance-based recognition approach of human motion and gestures of several peoplespsila several actions from uncalibrated camera by employing motion history-based representation. It employs the basic motion history image-based and the directional motion history image-based representation and then exploits these motion templates to recognize various motions having more than one motion direction or complex motion. Traditionally, the basic MHI approach used seven Hu moments for feature vector calculation. This paper analyzed the implementation of a better feature vector calculation for our directional approach. We tried with two different feature vector sets for Hu moment. Moreover, due to its better performance, we computed another feature vector set based on the top twelve orders of Zernike moments. Due to computational cost, we finally ignored to employ Zernike moments for the DMHI template for recognition. This new feature vector calculation approach can reduce the calculation and shows good recognition rate. Finally, this paper raised some future concerns of this method.

Template-based human motion recognition for complex activities

We have presented motion history-based human motion recognition technique with various formats of feature vectors. Since the inception of the motion history image (MHI) template for motion recognition, various progresses have been adopted to improve this basic MHI. Stages of development of appearance-based representation and recognition approach are presented here on the basic motion history-based approach to solve self-occlusion problem using our method. Excellent recognition rate for various motions has been found. This is based on gradient-based optical flow calculation. For recognition, Hu moments are considered to calculate feature vectors. Various feature vectors are considered in this paper.

Directional motion history templates for low resolution motion recognition

Human motion recognition in low-resolution video is very difficult task because due to low-resolution we miss much significant motion information. In this paper, we demonstrated appearance-based directional motion history image (DMHI) method to recognize various levels of video resolutions. The DMHI technique can overcome the self-occlusion problem that arises from motion overwriting. We found that it can significantly solve motion overwriting problem and can recognize complex actions or activities. We employed the same datasets with various levels of resolutions to test the DMHI and achieved satisfactory result up to a limit. When the resolution is very low, due to significant loss in motion information, we came across some difficulties to recognize the actions properly.

SURF-based spatio-temporal history image method for action representation

Researches on action understanding and analysis are very crucial for various applications in computer vision. However, these face numerous challenges to represent and recognize different complex actions. This paper presents a noble spatio-temporal 3D (XYT) method for recognizing various complex activities, with a blend of local and global feature-based approach for motion representation. We incorporate SURF (Speeded-Up Robust Features), which is a scale- and rotation-invariant interest point detector and descriptor. Based on the interest points, optical flow-based directional motion history and energy images are developed. In this approach, the flow-based motion vectors are split into four different channels. From these channels, the corresponding four directional templates are computed. 56-D feature vector is calculated according to the Hu invariants for each action. k-nearest neighbor classification scheme is employed for recognition. We employ leave-one-out cross-validation method for partitioning scheme. We apply our method to outdoor dataset and we achieve satisfactory recognition results. We compare our method with some of other approaches and show that our method outperforms them.

Action recognition with various speeds and timed-DMHI feature vectors

Usually, various motion recognition approaches can not perform well on actions that have variable speeds, or part of the dataset is speedy and vice versa. In this paper, we present the timing issue of our Directional Motion History Image method. This method can solve overwriting or motion self-occlusion problem significantly and thereby it performs well for complex and repetitive activities. However, it is important to analyze the method with activities having various speeds to show its robustness. The experimental results demonstrate that it can perform well with variable paces of the actions though the recognition rate is compromised a bit compared to the dataset having usual speed. We also improve the classification method with the incorporation of motion duration in the final feature vector so that for similar type of activities having different pace, it can show better result. Experiments on another dataset show better performance with the timing incorporation. The achieved recognition rates are very encouraging for further research and implementation is some intelligent systems for surveillance and related applications.