A Novel Approach for Tracking with Implicit Video Shot Detection

Abstract

Video shot detection - Shot change detection is an essential step in video content analysis. The field of Video Shot Detection (VSD) is a well exploited area. In the past, there have been numerous approaches designed to successfully detect shot boundaries for temporal segmentation. Robust Pixel Based Method is used to detect shot changes in a video sequence. Tracking algorithm is a time consuming process due to the large amount of data contained in video using the video shot detection the computational cost can be reduced to a great extent by the discarding the frames which are not of any interest for the tracking algorithm. In this paper we present a novel approach of combining the concepts of Video shot detection and Object tracking using particle filter to give us a efficient Tracking algorithm with implicit shot detection. Keywords - Bhattacharyya distance, Local adaptive threshold, Particle filter, Robust Pixel Difference method, Residual Re-sampling, Shot detection. I. INTRODUCTION The rapid development of storage and multimedia technologies has made the retrieval and processing of videos relatively easy. Temporal segmentation is a fundamental step in video processing, and shot change detection is the most basic way to achieve it. However, while hard cuts (abrupt transitions) can be easily detected by finding changes in a color histogram, gradual transitions such as dissolves, fades, and wipes are hard to locate. In practice, however, 99% of all edits fall into one of the following four categories hard cuts, fades, wipes and dissolves. Many shot change detection studies focus on finding low-level visual features, e.g., color histograms and edges, and then locate the spots of changes in those features. We focus on using Robust Pixel Method for shot detection. The conventional shot detection method using pixel wise comparison is not very efficient since it doesn't provide noise tolerance and because of its global thresholding nature. Many scenes involving sudden illumination changes such as lighting etc false trigger a shot change in the conventional method. The robust pixel method used in this paper provides threshold for noise and also locally adaptive threshold which makes it effective in situations where the conventional method fails. Object tracking is an important task in the field of computer vision. It generates the path traced by a specified object by locating its position in each frame of the video sequence. The use of Object tracking is pertinent in many vision applications such as automated surveillance, video indexing, vehicle navigation, motion based recognition, security and defence areas. Occlusion and noise are generally the biggest problems in any target tracking implementation. Tracking algorithms robustness is a measure of how well it continues to track and when it loses its target. Tracking is the observation of person(s) or object(s) on the move and supplying a timely ordered sequence of respective location data to a model under consideration. It is the process of locating a moving human or object over time using a camera. It is based on computer vision. Image registration is the basic step used in tracking application. It is a process that finds the location where a good matching is obtained by matching the template over the searching area of an input image. Registration algorithm fails in complex situations and loses the target in presence of noise, scaling and transformation changes. To address the above mentioned problems, we use a particle filter based tracking method for efficient tracking. Video shot detection and tracking algorithms have both been extensively researched and have been used in real world applications individually. Very less effort has been made to combine the concepts of video shot detection and tracking which can be of great help in real-world as both the technologies complement each other. Combining the two concepts guarantees a computationally quicker, cost effective solution for tracking on large video database with minimal pre-processing. In this paper Section II covers the concepts of Video shot detection using robust pixel difference method and also demonstrates it effectiveness with results. Section III focuses on concepts of tracking algorithm with results. Section IV elaborates the method of combining the two approaches where tracking algorithm is initiated after every shot change hence serving its final purpose of computationally efficient shot detection cum tracking system.