An Intruder Detection Algorithm for Vision Based Sense and Avoid System

Intruder detection is a crucial problem in vision based Sense and Avoid (SAA) system. In this paper a deep feature learning based intruder detection algorithm is proposed. The intruder detection algorithm contains four parts: obtaining the test samples, creating the overcomplete dictionary, deep feature learning and determining the region of the intruder. The sliding window technique is adopted to obtain the test samples. The K-means Singular Value Decomposition (K-SVD) is used for overcomplete dictionary training. We employ the deep feature learning method on the basis of the dictionary for feature extraction. The support vector machine (SVM) is used to select the region of interest (ROI), and the region of the intruder is finally determined by merging the overlapping ROIs. The experiment results indicate that the algorithm is robust under different weather and illumination conditions and different angles of view.