This paper presents an improved frame-to-frame (F-2-F) compressed video matching technique based on local features extracted from reduced size images, in contrast with previous F-2-F techniques that utilized global features extracted from full size frames. The revised technique addresses both accuracy and computational cost issues of the traditional F-2-F approach. Accuracy is improved through using local features, while computational cost issue is addressed through extracting those local features from reduced size images. For compressed videos, the DC-image sequence, without full decompression, is used. Utilizing such small size images (DC-images) as a base for the proposed work is important, as it pushes the traditional F-2-F from off-line to real-time operational mode. The proposed technique involves addressing an important problem: namely the extraction of enough local features from such a small size images to achieve robust matching. The relevant arguments and supporting evidences for the proposed technique are presented. Experimental results and evaluation, on multiple challenging datasets, show considerable computational time improvements for the proposed technique accompanied by a comparable or higher accuracy than state-of-the-art related techniques.
- Centre for Intelligent Visual Computing Research