V. Vijayakumar

Bio: V. Vijayakumar is an academic researcher from Bharathiar University. The author has contributed to research in topics: Association rule learning & Video processing. The author has an hindex of 4, co-authored 11 publications receiving 78 citations. Previous affiliations of V. Vijayakumar include Sri Ramakrishna Engineering College.

A study on video data mining

Abstract: Data mining is a process of extracting previously unknown knowledge and detecting the interesting patterns from a massive set of data. Thanks to the extensive use of information technology and the recent developments in multimedia systems, the amount of multimedia data available to users has increased exponentially. Video is an example of multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio. It is widely used in many major potential applications like security and surveillance, entertainment, medicine, education programs and sports. The objective of video data mining is to discover and describe interesting patterns from the huge amount of video data as it is one of the core problem areas of the data-mining research community. Compared to the mining of other types of data, video data mining is still in its infancy. There are many challenging research problems existing with video mining. Beginning with an overview of the video data-mining literature, this paper concludes with the applications of video mining.

A Novel Method for Super Imposed Text Extraction in a Sports Video

Abstract: Video is one of the sources for presenting the valuable information. It contains sequence of video images, audio and text information. Text data present in video contain useful information for automatic annotation, structuring, mining, indexing and retrieval of video. Nowadays mechanically added (superimposed) text in video sequences provides useful information about their contents. It provides supplemental but important information for video indexing and retrieval. A large number of techniques have been proposed to address this problem. This paper provides a novel method of detecting video text regions containing player information and score in sports videos. It also proposes an improved algorithm for the automatic extraction of super imposed text in sports video. First, we identified key frames from video using the Color Histogram technique to minimize the number of video frames. Then, the key images were converted into gray images for the efficient text detection. Generally, the super imposed text displayed in bottom part of the image in the sports video. So, we cropped the text image regions in the gray image which contains the text information. Then we applied the canny edge detection algorithms for text edge detection. The ESPN cricket video data was taken for our experiment and extracted the super imposed text region in the sports video. Using the OCR tool, the text region image was converted as ASCII text and the result was verified.

Speed Estimation and Detection of Moving Vehicles Based on Probabilistic Principal Component Analysis and New Digital Image Processing Approach

Abstract: In the twenty-first century, smart city surveillance management is one of the advancements of Information and Communication Technology. Intelligent Transport System (ITS) is an essential component of the smart city. Moving vehicle detection and speed estimation are major tasks of traffic management. Vehicle tracking and speed measurement methods failed to achieve good accuracy rate due to unsuccessful detection of moving vehicles. In the existing system, the conventional de-noising filters reduce the noise in smooth regions. The edges of object boundaries are not sharply identified. In this chapter, the Probabilistic Principal Component Analysis (PPCA) method is proposed to detect multiple outliers in objects. It is computationally fast and robust in identifying outliers which helps to reduce the dimension of video by finding an alternate set of coordinates. The proposed approach consists of three stages. First, Spatio-temporal Varying Filter (STVF) is applied to preprocess extracted frames. Contour finding algorithm is used to detect the vehicle. The frame count scheme is applied to estimate the vehicle speed. This approach provides high detection accuracy with high precision and recall rate in BrnoCompSpeed dataset.

A Study on Human Hair Analysis and Synthesis

Abstract: Human hair analysis and synthesis is an important and challenging problem in computer graphics and vision. This paper surveys the important topics of human hair analysis and synthesis: hair attributes, hair animation, hair simulation, hair rendering and applications. Because of difficulty, often unsolved problems that arise in all these areas.

Event Detection in Cricket Video based on Visual and Acoustic Features

Abstract: As digital video becomes more pervasive, efficient way of extracting the information from the video becomes an increasingly important. Video itself contains huge amount of data and complexity that makes the analysis very difficult. In this paper, we presented a video and audio features based event detection approach shown to be effective when applied to the cricket sports video. The advantage of this approach is the ability to recognize events that indicate high level of audio response and players crowd which can be correlated to key events. The proposed event detection has two steps. First, audio and visual features are extracted. Next, by defining a set of heuristic rules, the important semantic events such as wicket fall, score events are detected. The proposed framework has been tested with cricket video downloaded from the internet broadcast videos.

A survey on various approaches of text extraction in images

Abstract: Text Extraction plays a major role in finding vital and valuable information. Text extraction involves detection, localization, tracking, binarization, extraction, enhancement and recognition of the text from the given image. These text characters are difficult to be detected and recognized due to their deviation of size, font, style, orientation, alignment, contrast, complex colored, textured background. Due to rapid growth of available multimedia documents and growing requirement for information, identification, indexing and retrieval, many researches have been done on text extraction in images.Several techniques have been developed for extracting the text from an image. The proposed methods were based on morphological operators, wavelet transform, artificial neural network,skeletonization operation,edge detection algorithm, histogram technique etc. All these techniques have their benefits and restrictions. This article discusses various schemes proposed earlier for extracting the text from an image. This paper also provides the performance comparison of several existing methods proposed by researchers in extracting the text from an image.

EmoCo : Visual Analysis of Emotion Coherence in Presentation Videos

Abstract: Emotions play a key role in human communication and public presentations. Human emotions are usually expressed through multiple modalities. Therefore, exploring multimodal emotions and their coherence is of great value for understanding emotional expressions in presentations and improving presentation skills. However, manually watching and studying presentation videos is often tedious and time-consuming. There is a lack of tool support to help conduct an efficient and in-depth multi-level analysis. Thus, in this paper, we introduce EmoCo , an interactive visual analytics system to facilitate efficient analysis of emotion coherence across facial, text, and audio modalities in presentation videos. Our visualization system features a channel coherence view and a sentence clustering view that together enable users to obtain a quick overview of emotion coherence and its temporal evolution. In addition, a detail view and word view enable detailed exploration and comparison from the sentence level and word level, respectively. We thoroughly evaluate the proposed system and visualization techniques through two usage scenarios based on TED Talk videos and interviews with two domain experts. The results demonstrate the effectiveness of our system in gaining insights into emotion coherence in presentations.

Drone classification and identification system by phenome analysis using data mining techniques

Abstract: The Unmanned Aerial Vehicle (UAV) technology has gained immense popularity in recent times. The technology has witnessed enhanced capabilities in terms of payload, greater distance operability and stability in hovering. The growing sophistication in the UAV platform can be compared to the stability and efficiency delivered by manned aircraft. The UAV platform complimented by its size, absence of thermal signature and battery operated motors offer it incredible amount of stealthy performance capabilities. Recent times have been a witness to the prowess of UAV technology being exploited for illegal activities such as criminal, terrorist purposes, and drug delivery platform across the border. The drone technology has also been misused as a platform by, nefarious individuals who use the UAV technology with a unique eves dropping payload to launch network intrusion into corporations by hovering their UAV over the corporate infrastructure. According to Federal Aviation Administration (FAA), commercial airline pilots have reported over a 100 close call situations or drones in visible proximity of the aircrafts at the airports. Sales of Drone have been estimated to cross the 700,000 mark in 2015, an increase of 63% over its sales in 2014. This paper outlines the challenges in drone detection, its identification and classification to preempt any possible malicious intent.