International Journal of Computer and Applications

In the multimedia era a large volume of video data can be recorded during a certain period of time by multiple cameras. Such a rapid growth of video data requires both effective and efficient multiview video summarization techniques. The users can quickly browse and comprehend a large amount of audiovisual data. It is very difficult in real-time to manage and access the huge amount of video-content-handling issues of interview dependencies significant variations in illumination and presence of many unimportant frames with low activity. In this paper we propose a local-alignment-based FASTA approach to summarize the events in multiview videos as a solution of the aforementioned problems. A deep learning framework is used to extract the features to resolve the problem of variations in illumination and to remove fine texture details and detect the objects in a frame. Interview dependencies among multiple views of video are then captured via the FASTA algorithm through local alignment. Finally object tracking is applied to extract the frames with low activity. Subjective as well as objective evaluations clearly indicate the effectiveness of the proposed approach. Experiments show that the proposed summarization method successfully reduces the video content while keeping momentous information in the form of events. A computing analysis of the system also shows that it meets the requirement of real-time applications.

F-DES: Fast and Deep Event Summarization

A survey of teaching–learning-based optimization

While software defined network (SDN) brings more innovation to the development of future networks, it also faces a more severe threat from DDoS attacks. In order to deal with the single point of failure on SDN controller caused by DDoS attacks, we propose a framework for detection and defense of DDoS attacks in the SDN environment. Firstly, we deploy a trigger mechanism of DDoS attack detection on data plane to screen for abnormal flows in the network. Then, we use a combined machine learning algorithm based on K-Means and KNN to exploit the rate characteristics and asymmetry characteristics of the flows and to detect the suspicious flows determined by the detection trigger mechanism. Finally, the controller will take corresponding actions to defense against the attacks. In this paper, we propose a new framework of cooperative detection methods of control plane and data plane, which effectively improve the detection accuracy and efficiency, and prevent DDoS attacks on SDN.

A New Framework for DDoS Attack Detection and Defense in SDN Environment

New-flow based DDoS attacks in SDN: Taxonomy, rationales, and research challenges

Due to advancement in digital multimedia technologies, huge amount of video contents in such arenas as sports, surveillance, news, etc., is becoming widely available. Due to factors like presence of inactive frames, inter-view dependencies and frequent illumination changes, whole video content may not be important. Moreover, users may not have the adequate time to access/ store the entire video in real time. We propose a machine learning ensemble method to summarize the events in multiview videos, as a solution to the aforementioned issues. In order to make the accurate decision on keyframes, we trained our ensembles using meta approach where the inter-dependency and illumination changes of views are considered in the training phase. Experimental results on two benchmark datasets show that our model outperforms the state-of-the-art models with the best Recall and F-measure. The computing analysis of the model also indicates that it meets all requirements of real-time applications.

Event BAGGING: A novel event summarization approach in multiview surveillance videos

The rapid growth of video data demands both effective and efficient video summarization methods so that users are allowed to speedily browse and comprehend a large amount of video content. Hence, it is very challenging to store and access such audiovisual information in real time where an immense amount of recorded video content is rising within one second. In this paper we proposed an equal partition based clustering technique for summarizing the events in videos which can work better for real time applications (for e.g., surveillance video in various security systems). In clustering, the difficulty is to obtain the optimal set of clusters, which is gained by implementing Davies-Bouldin Index, a cluster validation technique which permits the users with free parameter based video summarization method for selecting the numbers of key–frames without incurring additional computational cost. The qualitative as well as quantitative evaluation is done in order to compare the performances of our proposed model and state-of-theart models. Experimental results on two benchmark datasets with various types of videos expose that the proposed method outperforms the state-of-the-art models with the best Precision and F–measure.

https://www.computer.org/csdl/pds/api/csdl/proceedings/download-article/12OmNALlcjK/pdf

Equal Partition Based Clustering Approach for Event Summarization in Videos

Commercial clustering of sustainable bamboo species in India

In this digital era, events based video summarization has become an emerging topic in the field of computer vision which finds its applications in video data management, highlights of a game, video...

ESUMM: Event SUMMarization on Scale-Free Networks

In this work, we propose an unsupervised learning network model based on Self-Organizing Map (SOM) to detect and summarize the events in multi-view surveillance videos. Action recognition in multi-view surveillance videos can be a primary element of a multimedia surveillance systems. The basic elements: feature extraction, structuring, mapping, and visualization are counted by our proposed model while video skimming as action recognition of the activities are subjected as the event detection and summarization. The model can work better for real-time applications, for e.g., surveillance, security systems, etc. The qualitative as well as quantitative assessment is done in order to compare the performances of our proposed SOM-based summarization technique and state-of-the-art models. Experimental results on three benchmark data-sets with various types of videos indicate that the proposed method outperforms the state-of-the-art models with the best F-measure.

Deepti D. Shrimankar

Papers

Event BAGGING: A novel event summarization approach in multiview surveillance videos

Equal Partition Based Clustering Approach for Event Summarization in Videos

Commercial clustering of sustainable bamboo species in India

ESUMM: Event SUMMarization on Scale-Free Networks

SOMES: An Efficient SOM Technique for Event Summarization in Multi-view Surveillance Videos