Abstract
Imagine you are asked to find out an unusual event in a daily recorded surveillance video. Questions aroused, how to detect events in a variety scenes?We focus our attention on finding out events that difference most from others and report it for further examinations. First we divide a video into several overlapping clips. Then we use optical flow to find out motion vectors of each frame in each clip. Magnitudes histogram, direction histogram and color histogram are selected as features. We form a similarity matrix by using difference and chamfer difference as the similarity measure of features in different clips. Then, we apply n-cut clustering .A threshold is selected to balance FAR (false alarm rate) and THR (true hit rate) according to ROC curve (receiver operating characteristic) and once a threshold is selected, clusters correspond to low self-similarity value is reported as unusual events and for further examination. Finally, this mechanism is tested on 6 different views.