Page 129 - ITU KALEIDOSCOPE, ATLANTA 2019
P. 129

ICT for Health: Networks, standards and innovation




           data structure. This data structure is stored in the cloud and
           both  MFPD  and  VPFD  access  the  data.  This  reduces  the        .     
           overall workload and allows multiple computers to run tasks   cos    =  |    ||    |                                                         (4)
           in parallel improving the processing speed. The map data
           structure is made persistent in order to last till the end of the   The person with an angle greater than the specific threshold
           video instead of removing the objects that have temporarily
           exited, which helps in handling short-term occlusions. Also,
           this  persistent  data  structure  allows  persons  to  be  tracked   Algorithm 3: Fall Detection
           through different cameras in the surveillance system.    -----------------------------------
                                                                  Input: curr – current frame index
           3.7    Fall Detection                                              seq – sequence of frames
                                                                              Pcurr – Currently detected persons
           The VPFD model constitutes two important phases: feature   Output: FP – Fallen person
           extraction using HoG and sequence analysis using LSTM.   Model fall = Trained HoG-LSTM model
           For  each  frame,  the  image  is  converted  to  grayscale  and   N = count (Pcurr)
           resized to a dimension of 640 x 480 in order to maintain the   T = time window
           same  dimension  of  the  resultant  feature  vector  during  the   max_angle = 0
           training and testing phase. The HoG feature vector is then   FP = None
           computed  from  the  reshaped  image  and  passed  on  to  the      data = empty array
           LSTM  model.  For  each  frame,  the  HoG  features  of  the      for j = {0,1, ……, T-1}
           previous  three  frames,  including  the  current  frame  are              feature = HoG(seq[i-j])
           considered as input to the LSTM model. Temporal sequence             data.append(feature)
           of  feature  vectors  is  taken  into  account  to  eliminate  false       end for
           detection,  thus  improving  the  capability  of  differentiating      val = Model fall (data)
           falls and actions of daily-living. The model output indicates      if val > threshold
           the occurrence of a fall. In order to map the fall with the           for k = {1,2, ……., N}
           detected person, angles of previous center coordinates are           angle = find_avg_angle (Pcurr[k].center, T)
           calculated as shown in Equation 2. The midpoint of the lower                 if max_angle < angle
           boundary line of the image is taken as the reference plane for          max_angle = angle
           angle computation as shown in Figure 3.
                                                                                      FP = k
                                                                                  end if
                                                                                end for
                                    Current                            end if
                   Previous         center                        return FP
                   center                 A                   is  determined  as  the  fallen  person  (Algorithm  3).  The
                    B                                         possibility of multiple persons falling at the same time can
                                                              also  be  taken  into  consideration  in  this  approach.  The
                   (x2, y2)          (x1, y1)
                                                              threshold  value  helps  in  eliminating  false  fall  detection
                                                              instances.
                                α
                                                              \

                                                                   4.  IMPLEMENTATION AND RESULTS
                      (x0, y0)   O       Reference plane
             Figure 3 – Angle between two centers of same person   The proposed system has been implemented and tested on
                                                              the Intel i5 processor CPU and Nvidia GeForce 940MX GPU
           Vector-based notation is utilized to represent both OA and   over standard datasets. The MFPT model is trained on the
           OB vectors respectively, as given in following equations.   Object  Tracking  Benchmark  (OTB  -  100)  dataset  and
                                                              Multiple  Object  Tracking  (MOT)  dataset.  The  UR  Fall
                   = (x1 − x0)   + (  1 −   0)                                  (2)   dataset  [21]  is  utilized  to  train  the  VPFD  model.  The
                                                              proposed  system  has  been  implemented  using  the  Python
                                                              programming  language.  FFMPEG  has  been  utilized  for
                   = (x2 − x0)   + (  2 −   0)                              (3)   video  encoding  and  decoding  purposes.  The  image  pre-
                                                              processing techniques are executed with the help of OpenCV
           The angle between vectors OA and OB can be obtained by   library. The Keras library in Python was used to create both
           finding the cosine inverse of the dot product of two vectors   the  CNN  and  LSTM  deep-learning  models.  The  overall
           as:                                                performance of the proposed system is shown in Figure 4,
                                                              where a red boundary indicates that a person is falling.






                                                          – 109 –
   124   125   126   127   128   129   130   131   132   133   134