The video tutorial explains the computation of Histogram of Oriented Gradients (HOG) features, a method used for object detection. It covers the division of images into blocks and cells, the computation of gradient vectors using derivative filters, and the process of voting and binning gradient directions. The tutorial also discusses the construction and normalization of HOG descriptors, their applications, and limitations. Practical implementations in MATLAB and OpenCV are mentioned, and the video concludes with an introduction to deep learning and CNNs.

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