Frame Difference based tracker stuck with first frame

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So I have been working these last few weeks on detecting and tracking motion on a video. My purpose is simple, detecting a dog once he moves and tracking it (with a rectangle box surrounding it). I only want to track one motion (the dog's) ignoring any other motion. After many unsuccessful trials with object and motion tracking algorithms and codes using Opencv, I came across something that I was able to modify to get closer to my goal. The only issue is that the code seems to keep the information from the first frame during the whole video, which causes the code to detect and put a rectangle in an empty area ignoring the actual motion.

Here's the code I'm using:

import imutils
import time
import cv2

previousFrame = None
count = 0
test = 0
temp_frame = None
rect = None

def searchForMovement(cnts, frame, min_area):

    global rect
    text = "Undetected"
    flag = 0

    for c in cnts:
        # if the contour is too small, ignore it
        if cv2.contourArea(c) < min_area:
            continue
        #print(c)

        #Use the flag to prevent the detection of other motions in the video
        if flag == 0:
            (x, y, w, h) = cv2.boundingRect(c)
            x = x - 100
            y = y - 100
            w = 400
            h = 400
            #print("x y w h")
            #print(x,y,w,h) 
            cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
            text = "Detected"
            rect = c
            flag = 1

    if text == "Undetected":
        (x, y, w, h) = cv2.boundingRect(rect)
        x = x - 100
        y = y - 100
        w = 400
        h = 400
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
        text = "Detected"



    if text == "Undetected":
        print (text, temp_frame)


    return frame, text

def trackMotion(ret,frame, gaussian_kernel, sensitivity_value, min_area):

    if ret:

        # Convert to grayscale and blur it for better frame difference
        # frame = cv2.bilateralFilter(frame, 7, 150, 150)
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        gray = cv2.GaussianBlur(gray, (gaussian_kernel, gaussian_kernel), 0)


        global previousFrame
        global count
        global test
        global temp_frame

        if previousFrame is None:
            previousFrame = gray
            return frame, "Uninitialized", frame, frame   

        frameDiff = cv2.absdiff(previousFrame, gray)
        thresh = cv2.threshold(frameDiff, sensitivity_value, 255, cv2.THRESH_BINARY)[1]

        thresh = cv2.dilate(thresh, None, iterations=2)
        _, cnts, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        frame, text = searchForMovement(cnts, frame, min_area)



        if text == "Detected":
            temp_frame = frame

        else:
            frame = temp_frame


        if text == "Undetected":
            print (text, temp_frame)


        if count % 10 == 0:
            previousFrame = gray

        count = count + 1

    return frame, text, thresh, frameDiff       


if __name__ == '__main__':

    video = "Track.avi"
    video0= "Track.mp4"
    video1= "Ntest1.avi"
    video2= "Ntest2.avi"

    camera = cv2.VideoCapture(video2)
    time.sleep(0.25)
    min_area = 5000 #int(sys.argv[1])



    while camera.isOpened():

        gaussian_kernel = 27
        sensitivity_value = 5
        min_area = 2500

        ret, frame = camera.read()

        #Check if the next camera read is not null
        if ret:
            frame, text, thresh, frameDiff = trackMotion(ret,frame, gaussian_kernel, sensitivity_value, min_area)


        else:
            print("Video Finished")
            close = False
            while not close:
                key1 = cv2.waitKey(3) & 0xFF
                if key1 == 27 or key1 == ord('q'):
                    close = True

            break

        cv2.namedWindow('Thresh',cv2.WINDOW_NORMAL)
        cv2.namedWindow('Frame Difference ...