This paper 1 introduces the system we developed for the Youtube-8M Video Understanding Challenge, in which a large-scale benchmark dataset [1] was used for multilabel video classification. The proposed framework contains hierarchical deep architecture, including the framelevel sequence modeling part and the video-level classification part. In the frame-level sequence modelling part, we explore ...

The International Classification of Epileptic Seizures is the most widely used, but an alternative system based purely on ictal symptoms and signs has been proposed: the semiological classification. Our objective was to compare the two in a sample of patients evaluated at epilepsy centers. We collected 78 consecutive patients evaluated in outpatient epilepsy clinics who subsequently underwent n...

Video scene segmentation is the first step towards automatic video annotation. For efficient video indexing and retrieval first step is to divide the video into shots and then classify the similar shots into scenes. In this paper we have discussed some existing algorithms for shot boundary detection, key frame extraction and classification of similar shots into scenes. The task of video segment...

Object detection and classification are the basic tasks in video analytics and become the starting point for other complex applications. Traditional video analytics approaches are manual and time consuming. These are subjective due to the very involvement of human factor. We present a cloud based video analytics framework for scalable and robust analysis of video streams. The framework empowers...

This paper reviews and analyses the problems facing video classification. It investigates how the semantic gap, the gap between formative measures and cognitive search queries, can be bridged. It presents a new taxonomy for video classification and concludes that narrowing the domain in a divideand-conquer manner is the current approach to bridging the semantic gap.

These Real-time video streaming over networks operates under stringent network resource constraints, with multiple video clients competing for limited network resources. In this paper, we study the problem of bandwidth allocation for video transmission over heterogeneous networks, with multiple video clients connecting to the video server simultaneously and demanding for the video services, and...

Video classification problem has been studied many years. The success of Convolutional Neural Networks (CNN) in image recognition tasks gives a powerful incentive for researchers to create more advanced video classification approaches. As video has a temporal content Long Short Term Memory (LSTM) networks become handy tool allowing to model long-term temporal clues. Both approaches need a large...

Video classification is highly important with wide applications, such as video search and intelligent surveillance. Video naturally consists of static and motion information, which can be represented by frame and optical flow. Recently, researchers generally adopt the deep networks to capture the static and motion information separately, which mainly has two limitations: (1) Ignoring the coexis...

MOVING OBJECT DETECTION, TRACKING AND CLASSIFICATION FOR SMART VIDEO SURVEILLANCE Yiğithan Dedeoğlu M.S. in Computer Engineering Supervisor: Assist. Prof. Dr. Uğur Güdükbay August, 2004 Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-ca...

The quality of the road pavement has always been one of the major concerns for governments around the world. Cracks in the asphalt are one of the most common road tensions that generally threaten the safety of roads and highways. In recent years, automated inspection methods such as image and video processing have been considered due to the high cost and error of manual metho...