Takehito Ogata

High-Speed Human Motion Recognition Based on a Motion History Image and an Eigenspace

This paper proposes an efficient technique for human motion recognition based on motion history images and an eigenspace technique. In recent years, human motion recognition has become one of the most popular research fields. It is expected to be applied in a security system, man-machine communication, and so on. In the proposed technique, we use two feature images and the eigenspace technique to realize high-speed recognition. An experiment was performed on recognizing six human motions and the results showed satisfactory performance of the technique.

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.

Improving human activity detection by combining multi-dimensional motion descriptors with boosting

A new, combined human activity detection method is proposed. Our method is based on Efros et al.s motion descriptors (2003) and Ke et al.s event detectors (2005). Since both methods use optical flow, it is easy to combine them. However, the computational cost of the training increases considerably because of the increased number of weak classifiers. We reduce this computational cost by extending Ke et al.s weak classifiers to incorporate multi-dimensional features. The proposed method is applied to off-air tennis video data, and its performance is evaluated by comparison with the original two methods. Experimental results show that the performance of the proposed method is a good compromise in terms of detection rate and of computation time of testing and training

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.

Comparative analysis between two view-based methods: MHI and DMHI

In this paper, we compare the basic motion history image (MHI) and our developed multi-directional motion history image (DMHI) for human gesture recognition. One of the constraints of the MHI is that it erases past motion by overwriting new motion onto the past one, thereby creating a template that does not correspond the motion properly. We have solved this overwrite problem by employing the concept of motion descriptors from optical flow vector. We have separated the optical flow vector into four components based on the four directions, namely up, down, left and right. We have employed Hu moments to calculate the feature vectors for both the MHI and the DMHI methods. We have experimentally verified the superiority of the DMHI method in terms of recognition rate for complex motion. In this paper, we have also analyzed the importance of motion energy image for both methods, and with different motions, we have found that presence of energy image is more evident in the DMHI technique than in the MHI technique.

Real-time human motion recognition by aerial robot

Automatic tracking of a specified person is an important technique especially in surveillance. Although various techniques for tracking a person employing a robot system are proposed, nobody attempts to recognize the motion of the tracked person yet. If a robot recognizes the motion of a tracked person, the robot would be able to realize a more intelligent tracking and actions. The purpose of this research is to propose a new tracking system employing a visually controlled aerial robot which recognizes a motion of the specified person in real-time. In this paper, we show the designed spherical aerial robot and the developed motion recognition method. The results of experiments using the developed system show satisfactory performance.

Human motions representation and recognition by directional motion history images

In this paper, we describe a technique for representing and recognizing human motions using directional motion history images. A motion history image is a single human motion image produced by superposing binarized successive motion image frames so that older frames may have smaller weights. It has, however, difficulty that the latest motion overwrites older motions, resulting in inexact motion representation and therefore incorrect recognition. To overcome this difficulty, we propose directional motion history images which describe a motion with respect to four directions of movement, i.e. up, down, right and left, employing optical flow. The directional motion history images are thus a set of four motion history images defined on four optical flow images. Experimental results show that the proposed technique achieves better performance in the recognition of human motions than the existent motion history images.