Videos are one of the prime and important sources of information. They can be availed in huge numbers. Whenever something is to be searched for in the video, the whole video clip has to be searched for which is a very time-consuming process. The search time could be reduced using video summarisation technique which is used to extract the relevant frames or the key-frames from the video in order to get the quick summary of the event. The relevant frames are extracted based on the particular object detection, face recognition, event detection, movements in the frames, etc. Supervised and unsupervised approaches are used to achieve the desired result. As the research got deeper in this area, new advanced and efficient technologies were invented. Deep learning feature extraction algorithms were found to be more efficient as compared to the conventional feature extraction algorithms. To make the video summarisation process faster, a parallel processing approach was also used by the researchers. This paper presents a review of different techniques used for video summarisation available in the literature. We have specifically presented a comparative study of video summarisation techniques implemented using parallel processing and multi-core CPUs. While the state-of-the-art of the field is presented through a comprehensive and systematic study, the paper also presents open issues, challenges, as well as open research avenues in the area. It is concluded that there is a particular scope of improvement in the processing time and use of parallelization in various stages of video summarisation. It is also concluded that the empirical study in the field should make use of a large size of video data set collected from multiple cameras rather than a small dataset collected using a single camera.

Usage of Parallelization Techniques for Video Summarisation: State-of-the-art, Open Issues, and Future Research Avenues

Video summary is a new content-based video compression technology, which can effectively find important information from the video and eliminate redundant data in video. The density peaks clustering (DPC) can quickly find the density peaks of datasets of arbitrary shapes and efficiently allocate data. In order to apply it to video summary generation, we consider the temporal characteristics of the video, and introduce it into the DPC algorithm, and propose an improved DPC algorithm with temporal characteristics (called T-DPC), which is applied for the Hue histogram clustering of video frames, and the video shot is segmented based on the clustering results. In the keyframe selection stage, calculate the similarity between each frame and its cluster center, and the entropy of each frame, then select the frame with the largest linear combination of entropy and similarity in each category as the keyframe. At the same time, the histogram intersection method is employed to remove similar frames in the keyframes to generate video summary. The proposed method in this paper is evaluated with 50 videos in the open video library. The experimental results show that the accuracy of the video summary generated by our method is higher than that of OV, STIMO, and VSUMM1, but not as good as DT and VSUMM2. The recall rate is higher than the OV, DT, and VSUMM2, the same as the STIMO, and slightly lower than the VSUMM1. The F values are all higher than the comparison algorithms OV, DT, STIMO, VSUMM1 and VSUMM2.

Video summary generation based on density peaks clustering with temporal characteristics

360° Video Summarization: Research Scope and Trends

With the popularization of online education, the number of educational videos is increasing, and some shoddy and dangerous videos also become potential threat to the mental health of students and the public security. Therefore, efficient video summarization technology is critical for the analysis, retrieval, and management of online educational videos. Online education videos usually belong to the screen content videos (SCV), and is typical real-time communication system, which urgently needs lightweight technologies with fast speed and low hardware requirements. Therefore, the traditional video summarization method for nature videos or with high complexity cannot be effectively applied for the online educational videos. SCV generated by screen recording the play of the Power Point (PPT-SCV) has also been widely applied in education field. Therefore, taking the PPT-SCV as an example, this paper proposed a lightweight video summarization method for the educational videos. First, the average standard deviation among frames and the variance of frame difference in a video clip which can effectively reflect the content characteristics of PPT-SCV were used to obtain the category of transitions and the key frames. Then, the subjective transition degree in video is used for marking the knowledge importance of each key frames, and a lightweight video summarization method was finally proposed. Experimental results demonstrated that the proposed method can efficiently locate the key frames which is in line with subjective perception.

A Lightweight Video Summarization Method Considering the Subjective Transition Degree for Online Educational Screen Content Videos

Today, System comprised of Surveillance cameras has become very useful and important in the every field, Mostly in the security industry. Also, Many numbers of surveillance cameras get added to the networks of surveillance or system every year as need and importance of surveillance cameras is increasing day by day. Video recorded from these surveillance cameras are large in size which require huge amount of time for monitoring and large storage space. Hence, there is a need of video summarization which has become very prominent since the last ten years because of the huge amount of available digital video content [3]. An algorithm we used for video summarization typically takes surveillance video as an input and extract a set of important frames or key-frames which is useful to represent the entire video content which are effectively more concise as compared to the original input video and convey semantic meaning. So, Our proposed solution aims at selecting keyframes from the video based on two criteria i.e. each object should appear within the scope of frame and each object should be visually presentable and must be closer to each other so that it could only show the related activities for ex. Summarization of video captured from ATM room camera should only display the part where user is interacting with the machine. So such a key frames are then used in final summarization.

Priyanka Rokade

Papers

Superintendence Video Summarization