Video Completion and Simultaneous Moving Object Detection for Extreme Surveillance Environments

Abstract

Since automated cleaning systems are less common in extreme surveillance environments, the accumulations of combustion fuels, dust, dirt, spider webs, etc., affect the visibility and clarity of the captured video data to different degrees resulting in incomplete (missing) video sequences. Grounded on this significant practical scenario, this letter proposes a scheme to concurrently complete the missing entries and detect moving objects with efficient background separation by formulating a single convex optimization problem developed and implemented in the tensor framework. The work is implemented as a unified scheme for concurrent video completion and moving object detection using twist spatio-temporal total variation to enhance the detection performance of the foreground while fitting the tensor nuclear norm minimization for efficient background separation with half thresholding applied on the tensor singular values. Moreover, the sparse variations that are part of the dynamic background are addressed using $l_{1/2}$ regularization. The formulated minimization problem is solved using the augmented Lagrangian method with an alternating direction strategy. The work also has computational benefits and the excellence of this method is revealed in the quantified performance evaluation against the compared approaches.