sanjaykthakur's profile - activity

I have a template image and a bunch of test images to detect if the template image is present in them, by matching the features detected by ORB. I use the following piece of code

import cv2
import numpy as np

template_gray = cv2.imread(template_path, 0)
feature_detector = cv2.ORB_create()
temp_kp, temp_des = feature_detector.detectAndCompute(template_gray, None)
threshold = len(temp_kp)/4

matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

test_gray = cv2_imread(<test_img_path>, 0)
test_kp, test_des = feature_detector.detectAndCompute(test_gray, None)
matches = matcher.match(temp_des, test_des)
if len(matches) > threshold: print("Object present")
else: print("Object missing")

I have tried many different combinations of threshold values. But none is working reliably across all test images. Too high of a threshold usually yields a lot of false negatives and too low of it gives too many false positives.

Can anyone tell me if there is a way to improve upon this situation or if this is the fundamental limitation of object detection by feature matching?

Object detection using ORB gives more than acceptable number of false positive and false negatives I have a template ima