Car tracking with cascades


Car Tracking with OpenCV
Car Tracking with OpenCV
In this tutorial we will look at vehicle tracking using haar features. We have a haar cascade file trained on cars.

The program will detect regions of interest, classify them as cars and show rectangles around them.

Related courses:

Detecting with cascades

Lets start with the basic cascade detection program:

#! /usr/bin/python
 
import cv2
 
face_cascade = cv2.CascadeClassifier('cars.xml')
vc = cv2.VideoCapture('road.avi')
 
if vc.isOpened():
    rval , frame = vc.read()
else:
    rval = False
 
while rval:
    rval, frame = vc.read()
 
    # car detection.
    cars = face_cascade.detectMultiScale(frame, 1.1, 2)
 
    ncars = 0
    for (x,y,w,h) in cars:
        cv2.rectangle(frame,(x,y),(x+w,y+h),(0,0,255),2)
        ncars = ncars + 1
 
    # show result
    cv2.imshow("Result",frame)
    cv2.waitKey(1);
vc.release()

This will detect cars in the screen but also noise and the screen will be jittering sometimes. To avoid all of these, we have to improve our car tracking algorithm.  We decided to come up with a simple solution.

Car tracking algorithm

For every frame:

  • Detect potential regions of interest
  • Filter detected regions based on vertical,horizontal similarity
  • If its a new region, add to the collection
  • Clear collection every 30 frames

 
Removing false positives
The mean square error function is used to remove false positives. We compare vertical and horizontal sides of the images. If the difference is to large or to small it cannot be a car.

ROI detection
A car may not be detected in every frame. If a new car is detected, its added to the collection.
We keep this collection for 30 frames, then clear it.

#! /usr/bin/python
 
import cv2
import numpy as np
 
def diffUpDown(img):
    # compare top and bottom size of the image
    # 1. cut image in two
    # 2. flip the top side
    # 3. resize to same size
    # 4. compare difference  
    height, width, depth = img.shape
    half = height/2
    top = img[0:half, 0:width]
    bottom = img[half:half+half, 0:width]
    top = cv2.flip(top,1)
    bottom = cv2.resize(bottom, (32, 64)) 
    top = cv2.resize(top, (32, 64))  
    return ( mse(top,bottom) )
 
 
def diffLeftRight(img):
    # compare left and right size of the image
    # 1. cut image in two
    # 2. flip the right side
    # 3. resize to same size
    # 4. compare difference  
    height, width, depth = img.shape
    half = width/2
    left = img[0:height, 0:half]
    right = img[0:height, half:half + half-1]
    right = cv2.flip(right,1)
    left = cv2.resize(left, (32, 64)) 
    right = cv2.resize(right, (32, 64))  
    return ( mse(left,right) )
 
 
def mse(imageA, imageB):
    err = np.sum((imageA.astype("float") - imageB.astype("float")) ** 2)
    err /= float(imageA.shape[0] * imageA.shape[1])
    return err
 
def isNewRoi(rx,ry,rw,rh,rectangles):
    for r in rectangles:
        if abs(r[0] - rx) < 40 and abs(r[1] - ry) < 40:
           return False  
    return True
 
def detectRegionsOfInterest(frame, cascade):
    scaleDown = 2
    frameHeight, frameWidth, fdepth = frame.shape 
 
    # Resize
    frame = cv2.resize(frame, (frameWidth/scaleDown, frameHeight/scaleDown)) 
    frameHeight, frameWidth, fdepth = frame.shape 
 
    # haar detection.
    cars = cascade.detectMultiScale(frame, 1.2, 1)
 
    newRegions = []
    minY = int(frameHeight*0.3)
 
    # iterate regions of interest
    for (x,y,w,h) in cars:
            roi = [x,y,w,h]
            roiImage = frame[y:y+h, x:x+w]   
 
            carWidth = roiImage.shape[0]
            if y > minY:
                diffX = diffLeftRight(roiImage)
                diffY = round(diffUpDown(roiImage))
 
                if diffX > 1600 and diffX < 3000 and diffY > 12000:
                    rx,ry,rw,rh = roi
                    newRegions.append( [rx*scaleDown,ry*scaleDown,rw*scaleDown,rh*scaleDown] )
 
    return newRegions
 
 
def detectCars(filename):
    rectangles = []
    cascade = cv2.CascadeClassifier('cars.xml')
    vc = cv2.VideoCapture(filename)
 
    if vc.isOpened():
        rval , frame = vc.read()
    else:
        rval = False
 
    roi = [0,0,0,0]
    frameCount = 0
 
    while rval:
        rval, frame = vc.read()
        frameHeight, frameWidth, fdepth = frame.shape 
 
        newRegions = detectRegionsOfInterest(frame, cascade)
        for region in newRegions:
            if isNewRoi(region[0],region[1],region[2],region[3],rectangles):
                rectangles.append(region)
 
        for r in rectangles:
            cv2.rectangle(frame,(r[0],r[1]),(r[0]+r[2],r[1]+r[3]),(0,0,255),3) 
 
        frameCount = frameCount + 1
        if frameCount > 30: 
            frameCount = 0
            rectangles = []
 
        # show result
        cv2.imshow("Result",frame)
        cv2.waitKey(1);
    vc.release()
 
detectCars('road.avi')

Final notes
The cascades are not  rotation invariant, scale and translation invariant. In addition, Detecting vehicles with haar cascades may work reasonably well, but there is gain with other algorithms (salient points).

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Download Code + Video + Cascade file


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40 thoughts on “Car tracking with cascades

  1. Frank - May 31, 2017

    try with both Python 2 and Python 3. Can you display the data beforehand?