Key frame,

In this paper, we propose a method for effectively tracking moving objects in videos captured using a moving camera in complex scenes. The video sequences may contain highly dynamic backgrounds and illumination changes. Four main steps are involved in the proposed method. First, the video is stabilized using affine transformation. Second, intelligent selection of frames is performed in order to extract only
those frames that have a considerable change in content. This step reduces complexity and computational time. Third, the moving object is tracked using Kalman filter and Gaussian mixture model. Finally object recognition using Bag of features is performed in order to recognize the moving objects.

Video indexing and retrieving is an important process towards searching in videos. Shot boundary detection approach is proposed to perform video indexing. To reduce the computational cost; frames that are clearly not shot boundaries are first removed from the original video. After that key points are found by dividing frame in to n*n blocks, and apply average function to each n*n block. Supervised learning classifier like support vector machine (SVM) is used for key points matching to capture different kinds of transitions such as abrupt (cut) and gradual (fade, wipe, dissolve).Frames shows transitions are represented in form of thumbnails. Audio characteristics like energy of signals are used to detect sound (tracks) in videos. Applications chosen for above approaches are CCTV and film videos.