Abnormal Acoustic Event Detection Based on Orthogonal Matching Pursuit in Security Surveillance System

This paper presents a feature extraction approach for surveillance system aimed at achieving the automatic detection and recognition of public security events. The proposed approach first generates a Gabor dictionary based on the human auditory critical frequency bands, and then uses the orthogonal matching pursuit (OMP) algorithm to sparse abnormal audio signal. We select the optimal several important atoms from the Gabor dictionary and extract the scale, frequency, and translation parameters of the atoms to form the OMP feature. The performance of OMP feature is compared with traditional acoustic features and their joint features, using support vector machine (SVM) and random forest (RF) classifiers. Experiments have been performed to evaluate the effectiveness of the OMP feature for supplementing traditional acoustic features. The results show the superior performance classifier for abnormal acoustic event detection (AAED) is RF. Furthermore, the introduction of the combined features addresses the problems of low recognition accuracy and poor robustness for the surveillance system in practical applications.