An Approach to Interesting Objects Detection in Low Quality Image Sequences for Fisheries Management

In order to extract interest objects in low quality image sequences from fisheries management, this paper proposes a new significant feature extraction method based on cascade framework. This algorithm involves preprocessing image sequences, clipping interesting areas, extracting SURF features, removing boundary features, and acquiring significant features with interesting objects. We apply our algorithm to fisheries management for counting and matching ships and cars, the proposed method can efficiently detect multiple objects from real-scene video frames with averaged accuracy 91.63%. Key–Words: SURF Features; Interest Objects; Features Extraction; Multiple Objects Detection; Low Quality Image Processing.