Activity-driven content adaptation for effective video summarisation

In this paper, we present a novel method for content adaptation and video summarization fully implemented in compressed-domain. Firstly, summarization of generic videos is modeled as the process of extracted human objects under various activities/events. Accordingly, frames are classified into five categories via fuzzy decision including shot changes (cut and gradual transitions), motion activities (camera motion and object motion) and others by using two inter-frame measurements. Secondly, human objects are detected using Haar-like features. With the detected human objects and attained frame categories, activity levels for each frame are determined to adapt with video contents. Continuous frames belonging to same category are grouped to form one activity entry as content of interest (COI) which will convert the original video into a series of activities. An overall adjustable quota is used to control the size of generated summarization for efficient streaming purpose. Upon this quota, the frames selected for summarization are determined by evenly sampling the accumulated activity levels for content adaptation. Quantitative evaluations have proved the effectiveness and efficiency of our proposed approach, which provides a more flexible and general solution for this topic as domain-specific tasks such as accurate recognition of objects can be avoided

Personalized video summarization based on group scoring

In this paper an expert-based model for generation of personalized video summaries is suggested. The video frames are initially scored and annotated by multiple video experts. Thereafter, the scores for the video segments that have been assigned the higher priorities by end users will be upgraded. Considering the required summary length, the highest scored video frames will be inserted into a personalized final summary. For evaluation purposes, the video summaries generated by our system have been compared against the results from a number of automatic and semi-automatic summarization tools that use different modalities for abstraction