Aircraft tracking (CV Project final part)

In this article, I’m going to present the final part of our computer vision project namely moving object detection, tracking, positioning, and speed estimation using only a single camera. This part contains an example of ‘visual aircraft tracking’. The project has been finally completed using python and opencv library. After giving the required reference libraries and summarizing the general steps, I’m going to share the main file which includes the main python codes.

As I have already mentioned in the previous posts, the first stage of the project is the automatic moving object detection. For this, I use a novel hybrid method which is a mixture of frame differencing and background subtraction approaches. The details could be seen in the following post:

In the post above, please download the ‘’ file in your working directory because we are going to need it for aircraft tracking.

The second step of the project is the object tracking part. The related post is the following:

In that post, I present a simple and lightweight tracker which is based on template matching, kalman filtering, and adaptive scaling. The required file in this post is ‘’ which includes both customTracker and customMultiTracker classes. Please also download this file into your working directory.

The third step of the project is the positioning (3D) of the moving objects using a single camera. In most cases, stereo and depth cameras are used to capture 3D images and depth information of the objects in the scene. However, using a single camera, it is not easy to accurately extract the depth information. Once it is extracted, then one can reconstruct the 3D position of the object by means of camera properties and mapping formulations. The velocity information can be then estimated using Kalman Filter for free. The following article includes the details of this step:

The required file for the post above is ‘’. Please also download the file into your working directory.

Both the tracking and localization parts use Kalman Filter which is presented in the following post:

Please also download the ‘’ file from the post given above. We are now ready for visual aircraft tracking. Please create a file namely ‘’ and copy-paste the following python codes:

import cv2
import numpy as np
import time
from customTracker import customTracker, customMultiTracker
from localization import localization
from objectDetector import objectDetector

class aircraftSpeedEstimation():
    def __init__(self):
        self.file_name = 'aircraft.mp4'
        self.frame_rate = 30
        self.capture = cv2.VideoCapture(self.file_name)
        # Check if camera opened successfully
        if (self.capture.isOpened()== False): 
            print("Error opening video stream or file")
        self.frame_name = 'Aircraft Speed Estimation'
        self.width = int(self.capture.get(cv2.CAP_PROP_FRAME_WIDTH))
        self.height = int(self.capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
        self.is_reading = False
        self.frame = None

        divx = cv2.VideoWriter_fourcc(*'DIVX')
        self.writer = cv2.VideoWriter('aircraft_cap.mp4',divx,20,(self.width,self.height))
        self.recording = False

        self.real_object_length = 40 #meters (aircraft length)
        self.selected_img = None
        self.tracking = False
        self.multiTracker = None  
        self.tracking_pen_color = (255,255,255)
        self.tracking_pen_thickness = 1
        self.auto_track = True

        self.localizer_list = []
        self.detector = objectDetector(self.width, self.height)
        self.search_duration = 0
        self.search_limit = 50 #frames

    def tracking_mode(self, frame):
            success, boxes = self.multiTracker.update(frame)
            if len(boxes) ==0:
                self.tracking = False
                self.search_duration = 0
            if success and len(boxes)> 0:
                for i, newbox in enumerate(boxes):
                    (x, y, w, h) = [int(v) for v in newbox]
                    cv2.rectangle(frame, (x, y), (x + w, y + h), self.tracking_pen_color,self.tracking_pen_thickness)
                    self.put_text(frame,'Tracking',(x-5,y-35), 0.3)
                    localizer = self.localizer_list[i]
                    pos, vel = localizer.predict(newbox,self.real_object_length)
                    text = 'Pos: {:.2f}, {:.2f}, {:.2f}'.format(pos[0],pos[1],pos[2])
                    self.put_text(frame,text, (x-5,y-20), 0.4)
                    speed = np.linalg.norm(np.array(vel)*3.6)
                    text = 'Speed: {:.2f} kph'.format(speed)
                    self.put_text(frame,text, (x-5,y-5), 0.4)
        except Exception as e:
            self.localizer_list = []
            self.tracking = False
            self.search_duration = 0
            print('tracking error : {} '.format(e))
    def put_text(self, frame, text, pos, scale):
    def init_tracking_mode(self,frame):
            print('tracks initiated......')
            # Create MultiTracker object
            #self.multiTracker = cv2.MultiTracker_create()
            self.multiTracker = customMultiTracker()
            self.localizer_list = []
            # Initialize MultiTracker 
            for bbox in self.detector.detection_list:
                tracker = customTracker()
                #tracker = cv2.TrackerKCF_create()
                localizer = localization(self.frame_rate, self.width, self.height)
        except Exception as e:
            print('tracking init error : {} '.format(e))
    def main_loop(self):
        self.is_reading, self.frame =
        self.tracking = False
        while self.is_reading:
            if self.is_reading:

                frame = self.frame
                    cv2.imshow(self.frame_name, frame)
                except Exception as e:
                    print('exception: ', e)

            key = (cv2.waitKey(20) & 0xFF)
            if key == ord('t') and self.tracking == False:
                self.tracking = True
            if key == ord('s'):
                self.tracking = False
            if key == ord('r'):
                if self.recording == False:
                    self.recording = True
                    self.recording = False
            if key == ord('q'):
            self.is_reading, self.frame =


    def state_machine(self,frame):
        if self.tracking == False:
            if self.auto_track == True:
                self.search_duration = self.search_duration + 1
                if self.search_duration> self.search_limit  and len(self.detector.detection_list)>0:
                    self.tracking = True
        if self.recording:

def main():
    ase = aircraftSpeedEstimation()
if __name__ == '__main__':

In the following figure, we see how an aircraft is tracked and position/speed information estimated. Please note that the detector’s ‘alpha_background’ parameter is set to 0.8 for fast objects such as aircrafts.

Aircraft tracking, localization and speed estimation using single camera

The video of the experiment and raw video can be seen in the following links. That’s all for this project, enjoy your day.

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