A video action recognition algorithm based on action segmentation and manifold metric learning is proposed to improve the accuracy of action recognition in videos. First

a video action segmentation algorithm based on analyzing the spreading area of actors' limbs is proposed to divide the video into segments that contain a specific action.The segmentation operation is used to recognize an action in the video quickly and reduce the mutual interference between adjacent actions.A silhouette of the actor in a frame is extracted using background subtraction method.Bounding boxes are generated in terms of the silhouettes.Given that silhouette extraction is affected by the background

the area function of the bounding boxes contains some noise

which can damage the regularity of the area function.After calculating the area value of the bounding box for each frame

the area function is smoothed using a robust weighted smooth method.Then

after extracting all the local minimum points of the smoothed area function

the second filter is used to remove fake local optimal points.After two filtering operations

the remaining minimum points are used as the segmentation position in the videos.Subsequently

the action recognition algorithm is independently implemented on each segment.For feature extraction and description of each segment

the Lucas-Kanade optical flow field is initially computed to obtain the velocity information of pixels for each frame in the segment.The pixels with non-zero magnitude of optical flow are considered as the interest points.Intraframe local curl and divergence

which is derived from the Lucas-Kanade optical flow field

are used to describe the motion relationship between interest points in the frame.A covariance matrix is formed for each action segment to fuse the features

including normalized global temporal features

normalized spatial features

optical flow

intraframe local curl

and divergence.The size of the final covariance is 7×7.Thus

the dimension of the feature covariance is relatively low.In this feature space

the action segment videos form a manifold.Several methods that measure the distance in the manifold space have been proposed.Generally

the distance between two points in a manifold space is the geodesic distance between them.In this study

a distance measurement method

which is obtained by supervised manifold metric learning

is proposed to further improve the accuracy of action classification.The LogDet divergence is utilized

and the action class labels are used to construct a constraint.A tangent space transfer matrix is obtained using the manifold metric learning.The tangent space transfer matrix leads distance calculation into a tangent space of a new latent manifold.Finally

the nearest neighbor classification method is used to recognize the actions. The three parts of the experiment are as follows.First

the efficiency of the action segmentation algorithm is evaluated on the Weizmann public video dataset.The results show that the proposed action segmentation method has acceptable segmentation capability.Second

the action reorganization comparison between with and without manifold metric learning on Weizmann dataset is performed to show the manifold metric learning performance.The action recognition accuracy without and with manifold metric learning is 92.8% and 95.6%

respectively

which indicates an improvement by 2.8%.Finally

the experimental results on KTH public video dataset verify the robustness of the proposed action recognition algorithm.The average recognition accuracy on KTH is 92.3%.On Weizmann and KTH datasets

the experimental comparisons indicated that the proposed algorithm is better than some state-of-the-art methods. The proposed action segmentation method based on analyzing the spreading area of actors' limbs can segment actions at the frame

where the limbs are closest to the body.Smoothing and the second filter step on the area function of the human bounding box enhance the action segmentation ability by anti-jamming.The segmentation method can obtain a desirable pre-processing effect.The multiple features fused effectively by the covariance matrix can describe the video action appropriately.The representation capability of the covariance matrix descriptor is further improved by adding optical flow

curl

and divergence information

which describe the motion direction information of the body parts of the body in detail.Evidently

the action recognition accuracy has been improved by using the manifold metric learning.The performance of the proposed action algorithm has been improved further by adding class-label information during the metric learning.All the experimental results show that the proposed video action recognition algorithm has high accuracy and desirable robustness.