JOINT OPTIMIZATION OF BACKGROUND SUBTRACTION AND OBJECT DETECTION FOR NIGHT SURVEILLANCE

Detecting foreground objects for night surveillance videos remains a challenging problem in scene understanding. Though many efforts have been made for robust background subtraction and robust object detection respectively, the complex illumination condition in night scenes makes it hard to solve each of these tasks individually. In practice, we see these two tasks are coupled and can be combined to help each other. In this work, we apply a recently proposed algorithm - Feedback Enabled Cascaded Classification Models (FECCM) - to combine the background subtraction task and the object detection task into a generic framework. The proposed framework treats each classifier for the respective task as a 'black-box', thus allows the usage of most existing algorithms as one of the classifiers. Experiment results show that the proposed method outperforms a state-of-the-art background subtraction method and a state-of-the-art object detection method.