Using Common Spatial Patterns to Select Relevant Pixels for Video Activity Recognition

Video activity recognition, despite being an emerging task, has been the subject of important research due to the importance of its everyday applications. Video camera surveillance could benefit greatly from advances in this field. In the area of robotics, the tasks of autonomous navigation or social interaction could also take advantage of the knowledge extracted from live video recording. In this paper, a new approach for video action recognition is presented. The new technique consists of introducing a method, which is usually used in Brain Computer Interface (BCI) for electroencephalography (EEG) systems, and adapting it to this problem. After describing the technique, achieved results are shown and a comparison with another method is carried out to analyze the performance of our new approach.

[1]  S. Swetha,et al.  Human action recognition from RGB-D data using complete local binary pattern , 2019, Cognitive Systems Research.

[2]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[3]  Dmitry Savransky,et al.  Detecting Planets from Direct-imaging Observations Using Common Spatial Pattern Filtering , 2019, The Astronomical Journal.

[4]  Thomas B. Moeslund,et al.  Selective spatio-temporal interest points , 2012, Comput. Vis. Image Underst..

[5]  Basilio Sierra,et al.  Video Activity Recognition: State-of-the-Art , 2019, Sensors.

[6]  Cheng Dai,et al.  Human action recognition using two-stream attention based LSTM networks , 2020, Appl. Soft Comput..

[7]  Gunnar Farnebäck,et al.  Two-Frame Motion Estimation Based on Polynomial Expansion , 2003, SCIA.

[8]  Jake K. Aggarwal,et al.  Human activity recognition from 3D data: A review , 2014, Pattern Recognit. Lett..

[9]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[10]  Keinosuke Fukunaga,et al.  Application of the Karhunen-Loève Expansion to Feature Selection and Ordering , 1970, IEEE Trans. Computers.

[11]  Basilio Sierra,et al.  Shedding Light on People Action Recognition in Social Robotics by Means of Common Spatial Patterns , 2020, Sensors.

[12]  Javier Muguerza,et al.  User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection Based on Estimation of Distributed Algorithms , 2016 .

[13]  Basilio Sierra,et al.  Dynamic selection of the best base classifier in One versus One , 2015, Knowl. Based Syst..

[14]  Muhammad Haroon Yousaf,et al.  Evaluating a bag-of-visual features approach using spatio-temporal features for action recognition , 2018, Comput. Electr. Eng..

[15]  Saleh A. Alshebeili,et al.  ECG-Based Subject Identification Using Common Spatial Pattern and SVM , 2019, J. Sensors.

[16]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[17]  Sung Wook Baik,et al.  Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features , 2018, IEEE Access.

[18]  Ying Wu,et al.  Robust 3D Action Recognition with Random Occupancy Patterns , 2012, ECCV.

[19]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[20]  Jenq-Neng Hwang,et al.  A Review on Video-Based Human Activity Recognition , 2013, Comput..