This paper presents a novel unusual behaviors detection algorithm to acquire biometric data for intelligent surveillance in real-time. Our work aims to design a completely unsupervised method for detecting unusual behaviors without using any explicit training dataset. To this end, the proposed approach learns from the behaviors recorded in the history; such that the definition of unusual behavior is modeled according to previous observations, but not a manually labeled dataset. To implement this, pyramidal
is employed to estimate the optical flow between consecutive frames, the results are encoded into flow histograms. Leveraging the correlations between the flow histograms, unusual actions can be detected by applying
principal component analysis
(PCA). This approach is evaluated under both indoor and outdoor surveillance scenarios. It shows promising results that our detection algorithm is able to discover unusual behaviors and adapt to changes in behavioral pattern automatically.