Tag: cars

Car tracking with cascades

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 course:
Master Computer Vision with OpenCV

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.

Related course:
Master Computer Vision with OpenCV

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) &lt; 40 and abs(r[1] - ry) &lt; 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).