I know that we can do feature matching across a temporal sequence of images to check which features belong to the same object.
So, for example, in the first image, I can create a bounding box for an object, and then I can run SIFT on that image and get the features inside the bounding box for that image. Then, I can check which features in the next image and the ones after that match with the set of features in my first image (the ones inside the bounding box) and get bounding boxes for the object in the subsequent frames.
However, I was wondering if there is a deep learning-based method to achieve the same result in an arguably better way.
Dear Muhammad Sarim Mehdi ,
Look the link, maybe useful.
https://learn.alwaysai.co/image-classification-ga?utm_campaign=CV%20Services%20-%20GA&utm_source=ppc&utm_medium=CV%20Services&utm_term=Image%20Classification&utm_campaign=CV+Services&utm_medium=ppc&utm_source=adwords&utm_term=segmentation%20in%20image%20processing&hsa_mt=b&hsa_net=adwords&hsa_ver=3&hsa_kw=segmentation%20in%20image%20processing&hsa_acc=9308646013&hsa_grp=89738834623&hsa_tgt=kwd-320488535439&hsa_src=g&hsa_ad=413274279599&hsa_cam=9005147173&gclid=CjwKCAiAzrWOBhBjEiwAq85QZ8EsZnJzU-Ci5kO5XfGchujl7A_VE6YEGHBmaXXfIb80Kr6Q9DaSjBoC0bMQAvD_BwE
Regards,
Shafagat
Dear Sarim Mehdi,
Your query is related to the data preprocessing. The deep learning algorithm is an application for training data. Related to your query, I suggest you to follow https://medium.com/@a.karazhay/guide-augment-images-and-multiple-bounding-boxes-for-deep-learning-in-4-steps-with-the-notebook-9b263e414dac
https://www.pyimagesearch.com/2020/10/05/object-detection-bounding-box-regression-with-keras-tensorflow-and-deep-learning/
etc.
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