Shot boundary detection servers as a preliminary step to structure the content of videos. Up to now, a large number of methods have been proposed. We give a brief overview of previous works with a novel view, focusing on the solutions of the two main disturbances, i.e., abrupt illuminance change and great camera or object motion. Then this paper presents a novel shot boundary detection framework, consisting of three components: fade out/in (abbreviated as FOI) detector, cut detector and gradual transition (abbreviated as GT) detector. The key technique of FOI detector is the recognition of monochrome frames. For cut detection, a second-order difference method is firstly applied to obtain candidate cuts, and then a post-processing procedure is taken to eliminate the false positives. In GT detector, the twin-comparison approach is employed to detect short gradual transition which lasts less than six frames, while for long gradual transition, an improvement of twin-comparison algorithm is designed. Firstly, to effectively reduce the false alarms of quick motion, the lower threshold is self-adaptive to motion feature. Secondly, an FSA (finite state automata) model is adopted to replace the twin-comparison strategy. This framework makes good use of various features and successfully integrates all the modules together. Finally, the system is evaluated on the TRECVID benchmarking platform and the experimental results reveal the effectiveness of our system.