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Connecting physical and virtual worlds




                                                                     Computational Science and Engineering (CSE) and
                     PERFOMANCE ANALYSIS                             IEEE International Conference on Embedded and
                                                                     Ubiquitous Computing (EUC), 2019, pp. 447-452.
            100
             80                                                   [4] Lvcai Chen,  Chunyan Yu, Li Chen, “A Multi-
             60                                                      Person  Pose  Estimation with LSTM for Video
                                                                     Stream” IEEE  International Conference on
             40
                                                                     Electronic Information Technology and Computer
             20
                                                                     Engineering, 2019.
              0
                    Training Accuracy   Testing Accuracy          [5] Yan Bin Ng, Basura Fernando, “Forecasting future
                                                                     action sequences with attention: a new approach to
                     SCNN    DBiLSTM    SAF + BiLSTM                 weakly supervised action forecasting” IEEE Trans.
                                                                     on Image Processing, Vol.29, Sep. 2020.
                   Figure 8 – Training and test accuracies        [6] Yan  Fu, Tao Liu,  Ou Ye, “Abnormal activity
                                                                     recognition based on deep learning in crowd” IEEE
                      Table 2 – Accuracy comparison                  International Conference on Intelligent Human-
                                                                     Machine Systems and Cybernetics, 2019.
              S. No.         Method            Accuracy %
               1          Sequential CNN          93.5            [7] Recommendation  ITU-T H.627, “Signalling and
               2            DBiLSTM               95.7               protocols for a video surveillance system”, August
                                                                     2020.
               3          SAF + Bi-LSTM           97.2
                            5.  CONCLUSION                        [8] Hui Tang, Qing Wang, Hong Chen, “Research on
                                                                     3D Human Pose Estimation Using RGBD Camera”
           The hybrid system for an action recognition system is built   IEEE Conference on Electronics Information and
           using a combination of SAF and Bi-LSTM. The VAR is used   Emergency Communication, 2019.
           for activity  forecasting, and feature extraction techniques
           were oriented towards improving the recognition of actions   [9] Jie Ou and  Hong Wu, “Efficient  Human Pose
           happening across the spatial and temporal region of video   Estimation with Depth wise Separable Convolution
           sequences. The activity forecasting helped in sustaining the   and Person  Centroid  Guided Joint Grouping”,
           system functioning in streamed video  while coping  with   arXiv:2012.03316v1, Dec. 2020.
           pause/missing data. The system  model  was trained  on
           different actions using the MSR action data set. The skeleton   [10] Linqin Cai, Sitong Zhou, Xun Yan, Rongdi Yuan,
           data of the video sequence  was used to  build the feature   "A  Stacked BiLSTM  Neural Network Based on
           vector  which was reduced using LDA  to improve the       Coattention Mechanism for Question Answering",
           efficiency of the classification. The system performance was   Computational Intelligence and Neuroscience, vol.
           evaluated on two different data sets of the MSR Action and   2019, Article ID 9543490, 2019.
           IIT-B Corridor  data set. The  SAF+Bi-LSTM model’s
           accuracy and precision suggests that multiple feature-based   [11] Seymanur Akti, Gozde  Ayse Tataroglu,  Hazim
           models help in achieving higher accuracies. The proposed   Kemal Ekenel, “Vision-based Fight Detection from
           system achieved 97.2% accuracy in action recognition. It can   Surveillance Cameras”, arXiv:2002.04355v1, Feb.
           be standardized  under Recommendation ITU-T  H.627        2020.
           “Signalling and protocols for a video surveillance system”.
                                                                  [12] WanruXu, Zhenjiang  Miao, and  Xiao- Ping,
                            REFERENCES                               “Hierarchical Spatio-Temporal  Model for Human
                                                                     Activity Recognition”, IEEE Trans. on Multimedia,
               [1] Video Surveillance Market,                        Feb. 2017.
                  https://www.marketsandmarkets.com/Market-
                  Reports/video-surveillance-market-645.html      [13] MSR Action Data Set:
                                                                     https://www.microsoft.com/en-
               [2] Q. Ke, M. Fritz, and B. Schiele, “Time-conditioned  us/download/details.aspx?id=52315
                  action anticipation in one shot,” in CVPR, 2019 pp.
                  9925–9934.                                      [14] MPII Human Pose Dataset: http://human-
                                                                     pose.mpi-inf.mpg.de/
               [3] Y. Zhang, A. Girgensohn and Y. Tjahjadi, "Activity
                  Forecasting in Routine Tasks by Combining Local  [15] IIT-B Corridor Dataset:
                  Motion Trajectories and  High-Level Temporal       https://drive.google.com/file/d/1HZZjINXIgWnq1
                  Models," 2019 IEEE International Conference on     FYuVTTBsfJiWsXy1uU5/view?usp=sharing




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