Tucker tensor decomposition-based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos

Abstract

The anomaly detection and localisation (ADL) gains remarkable interest as dealing with the complex surveillance videos for detecting the abnormal behaviour is tedious. The human effort in monitoring and classifying the abnormal object is inaccurate and time-consuming; therefore, the method is proposed using the Tucker tensor decomposition (TTD) and classification of the objects using Gaussian mixture model (GMM). Initially, the object is detected in the frames for easy recognition using simple background subtraction. The TTD decomposes the tensor as core tensor and factor matrices and the two decomposed tensors are compared using the cosine similarity measure that determines the location of the object in the frame. Finally, the features including shape and speed of the object are extracted that is used for classification using the GMM that follows the maximum posterior probability principle to detect and locate the anomaly in the video. The experimentation for anomaly detection proves that the proposed TTD and TTD-GMM method attains a higher rate of multiple object tracking precision, accuracy, sensitivity, and specificity at 0.96375, 0.975, 1, and 1, respectively. © The Institution of Engineering and Technology 2018.