Object detection and tracking in night time video surveillance

Abstract

Object tracking is always a challenging research to the computer vision community. It becomes more difficult at night video systems due to low contrast against the background. This paper is proposing a framework that detects object and tracks it at low contrast night surveillance video. A robust intensity statistics based detection method has been designed for processing low contrast frame and detect object structure from it. Based on successful detection, it tracks the object using Kalman filter algorithm.