Scale Invariant Feature Transform Flow trajectory approach with applications to human action recognition

Zhang, Jiatao; Tsoi, Ah Chung; Lo, Sio-Long

doi:10.1109/ijcnn.2014.6889596

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…Scovanner et al [40] proposed a 3D SIFT feature to better represent human actions. Wang et al and Zhang et al [41,42] applied SIFT flow to extract keypoint-based movement features between image frames and obtain better classification results than the features such as histogram of oriented gradients and histogram of optical flows. Local features have superior representability not only for visible-band images but also for NIR images.…”

Section: Pose Recognition By Computer Visionmentioning

confidence: 99%

Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis

Wang

Chen

2019

Electronics

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Sleep healthcare at home is a new research topic that needs to develop new sensors, hardware and algorithms with the consideration of convenience, portability and accuracy. Monitoring sleep behaviors by visual sensors represents one new unobtrusive approach to facilitating sleep monitoring and benefits sleep quality. The challenge of video surveillance for sleep behavior analysis is that we have to tackle bad image illumination issue and large pose variations during sleeping. This paper proposes a robust method for sleep pose analysis with human joints model. The method first tackles the illumination variation issue of infrared videos to improve the image quality and help better feature extraction. Image matching by keypoint features is proposed to detect and track the positions of human joints and build a human model robust to occlusion. Sleep poses are then inferred from joint positions by probabilistic reasoning in order to tolerate occluded joints. Experiments are conducted on the video polysomnography data recorded in sleep laboratory. Sleep pose experiments are given to examine the accuracy of joint detection and tacking, and the accuracy of sleep poses. High accuracy of the experiments demonstrates the validity of the proposed method.

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Section: Pose Recognition By Computer Visionmentioning

confidence: 99%

Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis

Wang

Chen

2019

Electronics

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“…In [1], SIFT flow, a strategy for video portrayal for human action recognition is used. This method helps in recognizing the movement between keypoints, also guides which points are invariant to scale changes, in two adjacent frames of a video, and it provides the conduct at keypoints and their neighbours.…”

Section: Related Workmentioning

confidence: 99%

“…Human action recognition is a vital as well as quite challenging problem in computer vision and machine learning [1]. When a training data set is available, the videos can be provided with labels, but these won't be as much informative.…”

Section: Problem Definitionmentioning

confidence: 99%

“…It has numerous applications including, video surveillance, perceiving gestures, human-computer interfacing and recognizing games [1]. Nonetheless, it is as yet a challenging issue on account of cluttered backgrounds, light changes, distinctive physiques of people, assortment of garments, camera movement, partial occlusions, scale variation of video screen, and so forth.Outlines, contours or optical flow are generally utilizes for representing the subtle details.…”

Section: Introductionmentioning

confidence: 99%

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Optical Flow For Action Recognition

Simon¹

2017

IJRASET

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Human Action Recognition is a developing field in computer vision and machine learning. Our aim is to perceive the game being played in the video. There are different procedures like extracting feature vector or key points for Human Action Recognit9ion. Optical flow is calculated, which gives the estimation of movement, of a pixel in a sequence of frames. The data set that we are utilizing is UCF Sports dataset. This is a popular human action recognition dataset. Labels can be provided but such labels are for humans and not for machines. The issue is that, if an unlabelled video is given, it is difficult for the machine to perceive the class of the video. In this paper, velocity profile of every pixel in several frames is calculated including magnitude and orientation. Further, orientations are divided into several bins on the basis of explicit results. These bins are used for detailed analysis and conditioning for particular human action depicted in the sports.

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“…Most of them have been just extended from existing image descriptors, such as 3D SIFT [20], and 3D HOG [12]. For example, Chuanzhen et al [15] used the 3D SURF descriptor to represent the local region of interest points, and Zhang et al [29] combined several descriptors such as HOG, HOF, and MBH with motion trajectories.…”

Section: Related Workmentioning

confidence: 99%