IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 7, 2013 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 1385 Abstract— A New simple approach to detect, classify shot boundaries and store shot boundary frames in Video sequence using human skin region detection based approach is proposed. Human skin region detection is the process of detecting skin region in sequence of frames. Skin region detection is mainly used for the identification of the human face detection. This approach is very much suitable for finding shots in TV News so that we can classify anchor and non-anchor frames to save the overall time which is required to watch overall news, Keywords: component; Shot Detection, Boundary, Frame, Skin region, Histogram. INTRODUCTION I. Face detection of TV News can be adapted for the Video shot detection refers to the detection of transitions between scenes in a digital video stream, the face detection plays plenty of applications in different areas like security and where we can value for the times by this we can save the time, efficient communication, memory management, person tracking, Today due to time and work commitment the people are not finding time to know what is happening in the society due to their busy work schedule so in order to know we have storing option with our communication device like TV but in order to know about it we can adapt video shot detection techniques. In case of TV news video we are not interested to watch the overall video whatever stored in our storing device the video will be consisted of anchors, reporter, advertise, etc. so we need to identify those video with the help of frame analysis by face detection. So when we reach the home by late night we can’t find the time to watch the overall TV news whatever we have stored in our device due to the advance technology like Tata sky, dish TV sun direct, etc. this are all work basically on storing concept instead of storing the whole video we can use video shot detection for the summarization of anchors, non- anchors, advertise and other related videos which are not required for us so that we can save the time not only with memory whatever for storing the whole news. In a similar manner we can adapt this video shot detection even for games to watch the particular bating or bowling or audience or etc. Let us consider the TV news video in which we know that anchor will be there to take care of whole video presentation for the corresponding news, the news video will be consisting of different frames included by anchors shot, reporter shot and advertisement so our technique will find the frames for the corresponding video and store them in separate folder. Generally speaking, the existing shot detection techniques can be classified into two categories: threshold based and machine learning based method. The former usually uses the frame differences for pixel, block-based or histogram comparisons and relies on the suitable threshold selection. However, it should be noted that threshold selection really is a hard problem and it usually depends on the test videos. The latter tries to overcome this drawback by machine learning. The proposed shot detection approach in this paper is based on the human skin detection to find the shot transition and non-shot transition. The rest of this paper is organized as follows. In section 2, we first introduce the framework. Then the skin region detection in section 3 in section 4 boundary detection is done based on the face detection in the frame. Conclusion is drawn in section 5. FRAMEWORK II. The proposed approach is applied in the uncompressed domain of video and consists of three modules, including decoding, human skin region detection and boundary detection as shown in the figure 1. The input video is first decoded into video frames than for the face detection there are lot many techniques here we using RGB to HSV color frames is used for the skin region detection. Find the skin region is present in the consecutive frames. Than the frame is considered as the boundary frame SKIN REGION DETECTION III. The first step is to classify each pixel in the frame as a skin or non-skin pixel. The second step is to identify different skin regions in the skin detected frames by using connectivity analysis. The last step is to decide whether each of skin region identified is a face or not. They are the height to width ratio of the skin region and the percentage of the skin in rectangle defined by the height and width. Fig. 1: The framework of the proposed method A. Skin Pixel classification Different color spaces used in skin detection, include HSV, normalized RGB, YCrCb, YIQ and CIELAB. According to Zarit et al. [8], HSV gives the best performance for skin pixel detection. Different color spaces used in skin detection, include HSV, normalized RGB, YCrCb, YIQ and CIELAB. According to Zarit et al. [8], HSV gives the best performance for skin pixel detection. In the HSV space, H stands for hue component, A Shot Boundary Detection Method for News Video Based Human Skin Region (Face) Detection Punith Kumar M. B. 1 Dr. P. S. Puttaswamy 2 1 Research Scholar 2 Professor 1 Dept. of ECE, BGSIT 2 Dept. of EEE, PESCE, Mandya, India