A Comparison of Corner Feature Detectors for Video Abrupt Shot Detection

Abstract

Comparison of feature detectors and evaluation of their performance is very important in computer vision. A new algorithm is proposed in this paper to compare the performance of four corner feature detectors based on abrupt shot boundary detection. The proposed algorithm consists of two stages: feature vectors generation where corner detector for all video frames is computed to obtain the descriptor feature vectors, and features matching where the number of matching features between two successive frames is calculated. The corner feature detectors used in this paper are BRISK, Harries, MinEigen, and FAST. Experimental results indicate that the proposed algorithm using MinEigen features detector provides better performance than other features detectors where the average value of recall, precision, and F measure is 0.99083, 0.98808, and 0.98875 for selected testing videos respectively. The results also show that the FAST is superior to others feature detectors when considering execution time.

Evaluation of Stereo Images Matching

“…The features can be an edge, a corner, an endpoint, a line or a curve, etc. [26]. Unlike area-based matching (ABM), which matches grey values directly, featurebased methods (FBM) match extracted features such as points, edges, or regions [1].…”