Image Processing: Algorithm Improvement for Real-Time FedEx Logo Detector

Question

I\'ve been working on a project involving image processing for logo detection. Specifically, the goal is to develop an automated system for a real-time FedEx truck/logo detector

Answer 1

You might want to take a look at feature matching. The goal is to find features in two images, a template image, and a noisy image and match them. This would allow you to find the template (the logo) in the noisy image (the camera image).

A feature is, in essence, things that humans would find interesting in an image, such as corners or open spaces. I would recommend using a scale-invariant feature transform (SIFT) as a feature detection algorithm. The reason I suggest using SIFT is that it is invariant to image translation, scaling, and rotation, partially invariant to illumination changes and robust to local geometric distortion. This matches your specification.

I generated the above image using code modified from the OpenCV docs docs on SIFT feature detection:

import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('main.jpg',0)  # target Image

# Create the sift object
sift = cv2.xfeatures2d.SIFT_create(700)

# Find keypoints and descriptors directly
kp, des = sift.detectAndCompute(img, None)

# Add the keypoints to the final image
img2 = cv2.drawKeypoints(img, kp, None, (255, 0, 0), 4)

# Show the image
plt.imshow(img2)
plt.show()

You will notice when doing this that a large number of the features do land on the FedEx logo (Above).

The next thing I did was try matching the features from the video feed to the features in the FedEx logo. I did this using the FLANN feature matcher. You could have gone with many approaches (including brute force) but because you are working on a video feed this is probably your best option. The code below is inspired from the OpenCV docs on feature matching:

import numpy as np
import cv2
from matplotlib import pyplot as plt

logo = cv2.imread('logo.jpg', 0) # query Image
img = cv2.imread('main2.jpg',0)  # target Image


# Create the sift object
sift = cv2.xfeatures2d.SIFT_create(700)

# Find keypoints and descriptors directly
kp1, des1 = sift.detectAndCompute(img, None)
kp2, des2 = sift.detectAndCompute(logo,None)

# FLANN parameters
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks=50)   # or pass empty dictionary
flann = cv2.FlannBasedMatcher(index_params,search_params)
matches = flann.knnMatch(des1,des2,k=2)

# Need to draw only good matches, so create a mask
matchesMask = [[0,0] for i in range(len(matches))]

# ratio test as per Lowe's paper
for i,(m,n) in enumerate(matches):
    if m.distance < 0.7*n.distance:
        matchesMask[i]=[1,0]

# Draw lines
draw_params = dict(matchColor = (0,255,0),
                   singlePointColor = (255,0,0),
                   matchesMask = matchesMask,
                   flags = 0)


# Display the matches
img3 = cv2.drawMatchesKnn(img,kp1,logo,kp2,matches,None,**draw_params)
plt.imshow(img3, )
plt.show()

Using this I was able to get the following features matched as seen below. You will notice that there are outliers. However the majority of features match:

The final step would then to be to simply draw a bounding box around this image. I will link you to another stack overflow question which does something similar but with the orb detector. Here is another way to get a bounding box using the OpenCV docs.

I hope this helps!