Moving object detection is a technique used in computer vision and image processing. Multiple consecutive frames from a video are compared by various methods to determine if any moving object is detected.

Moving objects detection has been used for wide range of applications like video surveillance, activity recognition, road condition monitoring, airport safety, monitoring of protection along marine border and etc.

Moving object detection is to recognize the physical movement of an object in a given place or region.[2] By acting segmentation among moving objects and stationary area or region, the moving objects motion could be tracked and thus could be analyzed later. To achieve this, consider a video is a structure built upon single frames, moving object detection is to find the foreground moving target(s), either in each video frame or only when the moving target show the first appearance in the video.

I'm going to use Opnecv and Python combination to detect and track the objects based on the colour

Step 1: Drawing a Rectangle on the Recognized Object

if your pc don't have python or opencv please follow this below insstructables

here is the python code:

import cv2
import numpy as np 
cap = cv2.VideoCapture(0)
while True:
	_, frame = cap.read()
	hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
	lower_yellow = np.array([20, 110, 110])
	upper_yellow = np.array([40, 255, 255])
	yellow_mask = cv2.inRange(hsv, lower_yellow, upper_yellow)
	(_,contours,_) = cv2.findContours(yellow_mask, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
	for contour in contours: 		
		area = cv2.contourArea(contour)
		if(area > 800):
			x,y,w,h = cv2.boundingRect(contour)
			frame = cv2.rectangle(frame, (x,y),(x+w,y+h),(0,0,255),10)
	cv2.imshow("tracking", frame)
	k = cv2.waitKey(5) & 0XFF 
	if k == 27:
		break
cv2.destroyAllWindows()
cap.release()

Step 2: Trace Out the Path in Which the Object Has Mooved

to trace the path:

for i in range(1, len(center_points)):
        b = random.randint(230, 255)
        g = random.randint(100, 255)
        r = random.randint(100, 255)
        if math.sqrt(((center_points[i - 1][0] - center_points[i][0]) ** 2) + (
                (center_points[i - 1][1] - center_points[i][1]) ** 2)) <= 50:
            cv2.line(frame, center_points[i - 1], center_points[i], (b, g, r), 4)

Step 3: Integrating Both the Codes

i'm going to integrate the both the code

import cv2
import numpy as np
import random
from collections import deque
cap = cv2.VideoCapture(1)
# To keep track of all point where object visited
center_points = deque()
while True:
    # Read and flip frame
    _, frame = cap.read()
    frame = cv2.flip(frame, 1)
    # Blur the frame a little
    blur_frame = cv2.GaussianBlur(frame, (7, 7), 0)
    # Convert from BGR to HSV color format
    hsv = cv2.cvtColor(blur_frame, cv2.COLOR_BGR2HSV)
    # Define lower and upper range of hsv color to detect. Blue here
    lower_blue = np.array([100, 50, 50])
    upper_blue = np.array([140, 255, 255])
    mask = cv2.inRange(hsv, lower_blue, upper_blue)
    # Make elliptical kernel
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (15, 15))
    # Opening morph(erosion followed by dilation)
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
    # Find all contours
    contours, hierarchy = cv2.findContours(mask.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[-2:]
    if len(contours) > 0:
        # Find the biggest contour
        biggest_contour = max(contours, key=cv2.contourArea)
        # Find center of contour and draw filled circle
        moments = cv2.moments(biggest_contour)
        centre_of_contour = (int(moments['m10'] / moments['m00']), int(moments['m01'] / moments['m00']))
        cv2.circle(frame, centre_of_contour, 5, (0, 0, 255), -1)
        # Bound the contour with circle
        ellipse = cv2.fitEllipse(biggest_contour)
        cv2.ellipse(frame, ellipse, (0, 255, 255), 2)
        # Save the center of contour so we draw line tracking it
        center_points.appendleft(centre_of_contour)
    # Draw line from center points of contour
    for i in range(1, len(center_points)):
        b = random.randint(230, 255)
        g = random.randint(100, 255)
        r = random.randint(100, 255)
        if math.sqrt(((center_points[i - 1][0] - center_points[i][0]) ** 2) + (
                (center_points[i - 1][1] - center_points[i][1]) ** 2)) <= 50:
            cv2.line(frame, center_points[i - 1], center_points[i], (b, g, r), 4)
    cv2.imshow('original', frame)
    cv2.imshow('mask', mask)
    k = cv2.waitKey(5) & 0xFF
    if k == 27:
        break
cv2.destroyAllWindows()
cap.release()