How to calculate the distance between pedestrians and vehicles in each frame?-CodePudding

I have data from a drone. The first table has the pedestrians' data in each frame. The pedestrians' data has the pedestrian Id, frame, x_est, y_est, v_abs. The second table is the vehicles' data. The vehicles' data has the vehicle Id, frame, x_est, y_est, vel_est. For example, in frame number 1, I have 39 pedestrians and two vehicles. I want to create a new table that has the following information:

the first column is the distance between each pedestrian and every vehicle in each frame. For example, I have 39 pedestrians and 2 vehicles:

d1 = sqrt((ped1_x - veh1_x)^2 (ped1_y - veh1_y)^2)

d2 = sqrt((ped1_x - veh2_x)^2 (ped1_y - veh2_y)^2)

d3 = sqrt((ped2_x - veh1_x)^2 (ped2_y - veh1_y)^2)

d4 = sqrt((ped2_x - veh2_x)^2 (ped2_y - veh2_y)^2)

and so on

the second and third columns are the associated speed of the pedestrian and vehicles. For example, if i get d1 then I have to include the speed of ped1 and speed of veh1. If i get d2, then I have to include the speed of ped1 and speed of veh2 and so on.

I have 116 frames. I want to write code in python or Matlab to do these tasks. I tried python with the following code but didn't work because the vehicles' data has 182 rows and the pedestrians' data has 3950:

if peds["frame"] == vehs["frame"]:
    distance = math.sqrt(((peds.x_est-vehs.x_est)**2) ((peds.y_est - vehs.y_est))**2)

I'm thinking to add a for loop to loop over each frame. How to modify the code to loop over each frame and calculate the distances between these objects?

This is an example of the data.

Pedestrians data: id 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

frame 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

x_est 15.31251243 14.62957291 14.81940554 16.261254 14.25065235 13.71913744 12.77037156 11.82149333 11.02452266 10.4550769 9.962500442 10.56845947 11.17672903 11.70758495 14.28816743 13.41605196 11.6316746 11.4797624 13.3395301 10.98651306 11.89763531 12.46714593 13.90898049 11.17611058 9.20275126 11.10086732 11.66968305 12.88437259 13.37708455 14.17485782 14.81943565 15.65388549 17.7046208 18.76703333 19.22188971 19.03219028 19.63930168 19.26095788 20.54963754 21.87812196

y_est 24.04146967 23.85122822 22.59973819 21.46111998 22.25845431 22.52372129 22.37086056 22.82695733 22.7892778 22.78941678 21.42243054 21.49884121 21.46045752 21.53683577 19.86642817 19.75351002 19.29791125 17.24875682 16.56578255 14.97104209 10.11358571 9.733765266 9.165258769 8.102246321 8.216836276 6.659910277 6.774266283 2.865368769 3.05553263 4.459266668 4.193704362 4.420605884 4.26981189 3.547987191 4.042100815 4.876447865 5.294238544 6.090777216 3.966160063 4.762697865

v_abs 2.459007157 2.654334571 3.315403455 3.389573803 3.378566929 3.045539512 3.23011785 2.925099475 2.584998721 2.901642056 2.811892448 2.151019342 1.96347414 1.500567927 3.540985451 2.709992115 3.267972565 3.395063149 2.721779676 4.012212099 0.880854234 0.813933137 0.704372621 0.912089788 0.549592663 3.007799428 1.978963898 3.44757396 3.15737162 3.529382782 3.5556166 3.409764593 1.170765247 1.580709745 1.085228781 0.922279132 0.802698916 1.875894301 0.804975425 1.205954878

Vehicles data:

id 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901

frame 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20

x_est 13.12181538 20.79676544 13.01830532 20.73447182 12.92056089 20.65301595 12.77984823 20.52422202 12.67147093 20.45207468 12.56504216 20.34050999 12.45704283 20.23392863 12.35122477 20.13000728 12.24825411 20.03070371 12.15111691 19.9359101 12.04685347 19.8497257 11.93870377 19.7593045 11.82507185 19.66695714 11.70785362 19.57813133 11.60090539 19.48509113 11.48174963 19.39305883 11.36790853 19.29876475 11.26066404 19.21938896 11.14538066 19.12658484 11.02149472 19.02832259

y_est 12.48631346 12.04945225 12.50757522 12.09243053 12.53150136 12.14119099 12.56295128 12.20178773 12.59034906 12.22451609 12.60945482 12.2800529 12.61885415 12.31947563 12.62842376 12.35799492 12.64016339 12.40403046 12.66696466 12.44343686 12.67398026 12.4828893 12.69217314 12.52761456 12.70404418 12.56747969 12.71201387 12.60893079 12.7352435 12.65130145 12.75948092 12.68935761 12.77039761 12.72362537 12.78182668 12.76864139 12.80477996 12.80719663 12.82141401 12.8529026

vel_est 2.607251494 2.480379041 2.607160714 2.479543211 2.60660445 2.478939563 2.60984436 2.482517229 2.610124888 2.479412431 2.610034708 2.481993315 2.609925021 2.483303075 2.609504867 2.484305008 2.608686875 2.485197239 2.607293512 2.485028991 2.606477634 2.483509058 2.60665034 2.483007857 2.607657856 2.482442343 2.609280114 2.481348408 2.609374438 2.481088229 2.612094232 2.480242567 2.613246915 2.479486909 2.612946522 2.476581726 2.614972124 2.475903808 2.618889831 2.47716006

Thanks in advance

CodePudding user response：

Just think through the problem in words. For each pedestrian entry, for each car entry, if the frame number matches, compute the distance between them, and add a new row. Nothing to it.

ped_id =  "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39"

ped_frame = "1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1"

ped_x_est = "15.31251243 14.62957291 14.81940554 16.261254 14.25065235 13.71913744 12.77037156 11.82149333 11.02452266 10.4550769 9.962500442 10.56845947 11.17672903 11.70758495 14.28816743 13.41605196 11.6316746 11.4797624 13.3395301 10.98651306 11.89763531 12.46714593 13.90898049 11.17611058 9.20275126 11.10086732 11.66968305 12.88437259 13.37708455 14.17485782 14.81943565 15.65388549 17.7046208 18.76703333 19.22188971 19.03219028 19.63930168 19.26095788 20.54963754 21.87812196"

ped_y_est = "24.04146967 23.85122822 22.59973819 21.46111998 22.25845431 22.52372129 22.37086056 22.82695733 22.7892778 22.78941678 21.42243054 21.49884121 21.46045752 21.53683577 19.86642817 19.75351002 19.29791125 17.24875682 16.56578255 14.97104209 10.11358571 9.733765266 9.165258769 8.102246321 8.216836276 6.659910277 6.774266283 2.865368769 3.05553263 4.459266668 4.193704362 4.420605884 4.26981189 3.547987191 4.042100815 4.876447865 5.294238544 6.090777216 3.966160063 4.762697865"

ped_v_abs = "2.459007157 2.654334571 3.315403455 3.389573803 3.378566929 3.045539512 3.23011785 2.925099475 2.584998721 2.901642056 2.811892448 2.151019342 1.96347414 1.500567927 3.540985451 2.709992115 3.267972565 3.395063149 2.721779676 4.012212099 0.880854234 0.813933137 0.704372621 0.912089788 0.549592663 3.007799428 1.978963898 3.44757396 3.15737162 3.529382782 3.5556166 3.409764593 1.170765247 1.580709745 1.085228781 0.922279132 0.802698916 1.875894301 0.804975425 1.205954878"

veh_id = "900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901 900 901"

veh_frame = "1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20"

veh_x_est = "13.12181538 20.79676544 13.01830532 20.73447182 12.92056089 20.65301595 12.77984823 20.52422202 12.67147093 20.45207468 12.56504216 20.34050999 12.45704283 20.23392863 12.35122477 20.13000728 12.24825411 20.03070371 12.15111691 19.9359101 12.04685347 19.8497257 11.93870377 19.7593045 11.82507185 19.66695714 11.70785362 19.57813133 11.60090539 19.48509113 11.48174963 19.39305883 11.36790853 19.29876475 11.26066404 19.21938896 11.14538066 19.12658484 11.02149472 19.02832259"

veh_y_est = "12.48631346 12.04945225 12.50757522 12.09243053 12.53150136 12.14119099 12.56295128 12.20178773 12.59034906 12.22451609 12.60945482 12.2800529 12.61885415 12.31947563 12.62842376 12.35799492 12.64016339 12.40403046 12.66696466 12.44343686 12.67398026 12.4828893 12.69217314 12.52761456 12.70404418 12.56747969 12.71201387 12.60893079 12.7352435 12.65130145 12.75948092 12.68935761 12.77039761 12.72362537 12.78182668 12.76864139 12.80477996 12.80719663 12.82141401 12.8529026"

veh_vel_est = "2.607251494 2.480379041 2.607160714 2.479543211 2.60660445 2.478939563 2.60984436 2.482517229 2.610124888 2.479412431 2.610034708 2.481993315 2.609925021 2.483303075 2.609504867 2.484305008 2.608686875 2.485197239 2.607293512 2.485028991 2.606477634 2.483509058 2.60665034 2.483007857 2.607657856 2.482442343 2.609280114 2.481348408 2.609374438 2.481088229 2.612094232 2.480242567 2.613246915 2.479486909 2.612946522 2.476581726 2.614972124 2.475903808 2.618889831 2.47716006"

import math
import pandas as pd

def convert(s,cvt):
    return [cvt(k) for k in s.split()]

peds = list(zip( 
    convert(ped_id,int),
    convert(ped_frame,int),
    convert(ped_x_est,float),
    convert(ped_y_est,float),
    convert(ped_v_abs,float)
))

cars = list(zip( 
    convert(veh_id,int),
    convert(veh_frame,int),
    convert(veh_x_est,float),
    convert(veh_y_est,float),
    convert(veh_vel_est,float)
))

newdata = []
for ped in peds:
    for car in cars:
        if ped[1] != car[1]:
            continue
        dist = math.sqrt((ped[2]-car[2])**2   (ped[3]-car[3])**2)
        newdata.append( (ped[0], car[0], dist, ped[4], car[4]) )

df = pd.DataFrame( newdata, columns=("ped id", "car id", "distance", "ped vel", "car vel"))
print(df)

Output:

    ped id  car id   distance   ped vel   car vel
0        0     900  11.760986  2.459007  2.607251
1        0     901  13.186566  2.459007  2.480379
2        1     900  11.464494  2.654335  2.607251
3        1     901  13.316012  2.654335  2.480379
4        2     900  10.254910  3.315403  2.607251
..     ...     ...        ...       ...       ...
75      37     901   6.153415  1.875894  2.480379
76      38     900  11.303343  0.804975  2.607251
77      38     901   8.087069  0.804975  2.480379
78      39     900  11.675921  1.205955  2.607251
79      39     901   7.366554  1.205955  2.480379

[80 rows x 5 columns]