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Parameter Free Clustering Approach for Event Summarization in Videos

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1st International conference on computer vision and image processing (CVIP)

作者： Mishra, Deepak Kumar Singh, Navjot Natl Inst Technol Dept Comp Sci & Engn Srinagar Uttarakhand India

ISBN: (纸本)9789811021046;9789811021039

Digital videos, nowadays, are becoming more common in various fields like education, entertainment, etc. due to increased computational power and electronic storage capacity. With an increasing size of video collection, a technology is needed to effectively and efficiently browse through the video without losing contents of the video. The user may not always have sufficient time to watch the entire video or the entire content of the video may not be of interest of user. In such cases, user may just want to go through the summary of the video instead of watching the entire video. In this paper we propose an approach for event summarization in videos based on clustering method. Our proposed method provides a set of key frames as a summary for a video. The key frames which are closer to the cluster heads of the optimal clustering are combined to form the summarized video. The evaluation of the proposed model is done on a publicly available dataset and compared with ten state-of-the-art models in terms of precision, recall and F-measure. The experimental results demonstrate that the proposed model outperforms the rest in terms of F-measure.

关键词： Event summarization Key frames Clustering SD index Video skimming

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An Improved Contextual Information Based Approach for Anomaly Detection via Adaptive Inference for Surveillance Application

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1st International conference on computer vision and image processing (CVIP)

作者： Rao, T. J. Narendra Girish, G. N. Rajan, Jeny Natl Inst Technol Karnataka Dept Comp Sci & Engn Mangalore 575025 Karnataka India

ISBN: (纸本)9789811021046;9789811021039

Anomalous event detection is the foremost objective of a visual surveillance system. Using contextual information and probabilistic inference mechanisms is a recent trend in this direction. The proposed method is an improved version of the Spatio-Temporal Compositions (STC) concept, introduced earlier. Specific modifications are applied to STC method to reduce time complexity and improve the performance. The non-overlapping volume and ensemble formation employed reduce the iterations in codebook construction and probabilistic modeling steps. A simpler procedure for codebook construction has been proposed. A non-parametric probabilistic model and adaptive inference mechanisms to avoid the use of a single experimental threshold value are the other contributions. An additional feature such as event-driven high-resolution localization of unusual events is incorporated to aid in surveillance application. The proposed method produced promising results when compared to STC and other state-of-the-art approaches when experimented on seven standard datasets with simple/complex actions, in non-crowded/crowded environments.

关键词： Visual surveillance Anomalous event detection Spatio-temporal volume Video processing Bag of words Object detection Adaptive inference

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Scheimpflug Camera Calibration Using Lens Distortion Model

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1st International conference on computer vision and image processing (CVIP)

作者： Fasogbon, Peter Duvieubourg, Luc Macaire, Ludovic Univ Lille CNRS UMR 9189 Lab CRISTALCite Sci Bat P2 F-59655 Villeneuve Dascq France

ISBN: (纸本)9789811021046;9789811021039

Scheimpflug principle requires that the image sensor, lens, and object planes intersect at a single line known as "Scheimpflug line." This principle has been employed in several applications to increase the depth of field needed for accurate dimensional measures. In order to provide a metric 3D reconstruction, we need to perform an accurate camera calibration. However, pin-hole model assumptions used by classical camera calibration techniques are not valid anymore for Scheimpflug setup. In this paper, we present a new intrinsic calibration technique using bundle adjustment technique. We elaborate Scheimpflug formation model, and show how we can deal with introduced Scheimpflug distortions, without the need to estimate their angles. The proposed method is based on the use of optical lens distortion models. An experimental comparison of the proposed approach with Scheimpflug model has been made on real industrial data sets under the presence of large distortions. We have shown a slight improvement brought by the proposed method.

关键词： Camera calibration Optical distortions Bundle adjustment Scheimpflug

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Reconfigurable Architecture-Based Implementation of Non-uniformity Correction for Long Wave IR Sensors

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1st International conference on computer vision and image processing (CVIP)

作者： Khare, Sudhir Kaushik, Brajesh Kumar Singh, Manvendra Purohit, Manoj Singh, Himanshu Instruments Res & Dev Estab Dehra Dun 248008 Uttar Pradesh India Indian Inst Technol Roorkee 247667 Uttar Pradesh India

ISBN: (纸本)9789811021046;9789811021039

Infra Red (IR) imaging systems have various applications in military and civilian sectors. Most of the modern imaging systems are based on Infra Red Focal Plane Arrays (IRFPAs), which consists of an array of detector element placed at focal plane of optics module. Performance of IRFPAs operating in Medium Wave Infra Red (MWIR) and Long Wave Infra Red (LWIR) spectral bands are strongly affected by spatial and temporal Non-Uniformity (NU). Due to difference in the photo response of detector elements within the array, Fixed-Pattern Noise (FPN) becomes severe. To exploit the potential of current generation infrared focal plane arrays, it is crucial to correct IRFPA for fixed-pattern noise. Different Non-Uniformity Correction (NUC) techniques have been discussed and real-time performance of two-point non-uniformity correction related to IR band is presented in this paper. The proposed scheme corrects both gain and offset non-uniformities. The techniques have been implemented in reconfigurable hardware (FPGA) and exploits BlockRAM memories to store the gain and offset coefficients in order to achieve real-time performance. NUC results for long-range LWIR imaging system are also presented.

关键词： Infrared radiation

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A Novel Approach of an (n, n) Multi-Secret image Sharing Scheme Using Additive Modulo

引用

1st International conference on computer vision and image processing (CVIP)

作者： Deshmukh, Maroti Nain, Neeta Ahmed, Mushtaq Malviya Natl Inst Technol Jaipur Rajasthan India Natl Inst Technol Srinagar Uttarakhand India

ISBN: (纸本)9789811021046;9789811021039

Secret sharing scheme (SSS) is an efficient method of transmitting one or more secret images securely. The conventional SSS share one secret image to n participants. With the advancement of time, there arises a need for sharing multiple secret image. An (n,n)-Multi-Secret image Sharing (MSIS) scheme is used to encrypt n secret images into n meaningless shared images and stored it in different database servers. For recovery of secrets all n shared images are required. In earlier work n secret images are shared among n or n + 1 shared images, which has a problem as one can recover fractional information from less than n shared images. Therefore, we need a more efficient and secure (n, n)-MSIS scheme so that less than n shared images do not reveal fractional information. In this paper, we propose an (n, n)-MSIS scheme using additive modulo and reverse bit operation for binary, grayscale, and colored images. The experimental results report that the proposed scheme requires minimal computation time for encryption and decryption. For quantitative analysis Peak Signal to Noise Ratio (PSNR), Correlation, and Root Mean Square Error (RMSE) techniques are used. The proposed (n, n)-MSIS scheme outperforms the existing state-of-the-art techniques.

关键词： Secret sharing scheme Multi-secret image sharing scheme Additive modulo Correlation RMSE PSNR

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Crowd Disaster Avoidance System (CDAS) by Deep Learning Using eXtended Center Symmetric Local Binary Pattern (XCS-LBP) Texture Features

引用

1st International conference on computer vision and image processing (CVIP)

作者： Nagananthini, C. Yogameena, B. Thiagarajar Coll Engn Dept ECE Madurai Tamil Nadu India

ISBN: (纸本)9789811021046;9789811021039

In order to avoid crowd disaster in public gatherings, this paper aims to develop an efficient algorithm that works well in both indoor and outdoor scenes to give early warning message automatically. It also deals with high dense crowd and sudden illumination changing environment. To address this problem, first an XCS-LBP (eXtended Center Symmetric Local Binary Pattern) features are extracted which works well under sudden illumination changes. Subsequently, these features are trained using deep Convolutional Neural Network (CNN) for crowd count. Finally, a warning message is displayed to the authority, if the people count exceeds a certain limit in order to avoid the crowd disaster in advance. Benchmark datasets such as PETS2009, UCSD and UFC_CC_50 have been used for experimentation. The performance measures such as MSE (Mean Square Error), MESA (Maximum Excess over Sub Arrays) and MAE (Mean Absolute Error) have been calculated and the proposed approach provides high accuracy.

关键词： Crowd disaster Texture feature Convolutional neural network People counting

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Online Support Vector Machine Based on Minimum Euclidean Distance

引用

1st International conference on computer vision and image processing (CVIP)

作者： Dahiya, Kalpana Chauhan, Vinod Kumar Sharma, Anuj Panjab Univ Univ Inst Engn & Technol Chandigarh India Panjab Univ Comp Sci & Applicat Chandigarh India

ISBN: (纸本)9789811021046;9789811021039

The present study includes development of an online support vector machine (SVM) based on minimum euclidean distance (MED). We have proposed a MED support vector algorithm where SVM model is initialized with small amount of training data and test data is merged to SVM model for incorrect predictions only. This method provides a simpler and more computationally efficient implementation as it assign previously computed support vector coefficients. To merge test data in SVM model, we find the euclidean distance between test data and support vector of target class and the coefficients of MED of support vector of training class are assigned to test data. The proposed technique has been implemented on benchmark data set mnist where SVM model initialized with 20K images and tested for 40K data images. The proposed technique of online SVM results in overall error rate as 1.69% and without using online SVM results in error rate as 7.70%. The overall performance of the developed system is stable in nature and produce smaller error rate.

关键词： Support vector machines Online support vector machines Sequential minimal optimization Euclidean distance Classification

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Classification of Object Trajectories Represented by High-Level Features Using Unsupervised Learning

引用

1st International conference on computer vision and image processing (CVIP)

作者： Saini, Rajkumar Ahmed, Arif Dogra, Debi Prosad Roy, Partha Pratim IIT Roorkee Roorkee Uttar Pradesh India Haldia Inst Technol Haldia India IIT Bhubaneswar Bhubaneswar Orissa India

ISBN: (纸本)9789811021046;9789811021039

Object motion trajectory classification is an important task, often used to detect abnormal movement patterns for taking appropriate actions to prohibit occurrences of unwanted events. Given a set of trajectories recorded over a period of time, they can be clustered to understand usual flow of movement or detection of unusual flow. Automatic traffic management, visual surveillance, behavioral understanding, and sports or scientific video analysis are some of the typical applications that benefit from clustering object trajectories. In this paper, we have proposed an unsupervised way of clustering object trajectories to filter out movements that deviate large from the usual patterns. A scene is divided into nonoverlapping rectangular blocks and importance of each block is estimated. Two statistical parameters that closely describe the dynamic of the block are estimated. Next, these high-level features are used to cluster the set of trajectories using k-means clustering technique. Experimental results using public datasets reveal that, our proposed method can categorize object trajectories with higher accuracy when compared to clustering obtained using raw trajectory data or grouped using complex method such as spectral clustering.

关键词： Trajectory Surveillance Clustering k-means Label Node-no RAG

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Surveillance Scene Segmentation Based on Trajectory Classification Using Supervised Learning

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1st International conference on computer vision and image processing (CVIP)

作者： Saini, Rajkumar Ahmed, Arif Dogra, Debi Prosad Roy, Partha Pratim IIT Roorkee Roorkee Uttar Pradesh India Haldia Inst Technol Haldia India IIT Bhubaneswar Bhubaneswar Orissa India

ISBN: (纸本)9789811021046;9789811021039

Scene understanding plays a vital role in the field of visual surveillance and security where we aim to classify surveillance scenes based on two important information, namely scene's layout and activities or motions within the scene. In this paper, we propose a supervised learning-based novel algorithm to segment surveillance scenes with the help of high-level features extracted from object trajectories. High-level features are computed using a recently proposed nonoverlapping block-based representation of surveillance scene. We have trained Hidden Markov Model (HMM) to learn parameters describing the dynamics of a given surveillance scene. Experiments have been carried out using publicly available datasets and the outcomes suggest that, the proposed methodology can deliver encouraging results for correctly segmenting surveillance with the help of motion trajectories. We have compared the method with state-of-the-art techniques. It has been observed that, our proposed method outperforms baseline algorithms in various contexts such as localization of frequently accessed paths, marking abandoned or inaccessible locations, etc.

关键词： Trajectory Surveillance HMM Supervised classification Scene segmentation RAG

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Local Binary Pattern and Its Variants for Target Recognition in Infrared imagery

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1st International conference on computer vision and image processing (CVIP)

作者： Akula, Aparna Ghosh, Ripul Kumar, Satish Sardana, H. K. CSIR CSIO Chandigarh 160030 India Acad Sci & Innovat Res AcSIR Madras Tamil Nadu India

ISBN: (纸本)9789811021046;9789811021039

In this research work, local binary pattern (LBP)-based automatic target recognition system is proposed for classification of various categories of moving civilian targets using their infrared image signatures. Target recognition in infrared images is demanding owing to large variations in target signature and limited target to background contrast. This demands robust features/descriptors which can represent possible variations of the target category with minimal intra class variance. LBP, a simple yet efficient texture operator initially proposed for texture recognition of late is gaining popularity in face and object recognition applications. In this work, the suitability of LBP and two of its variants, local ternary pattern (LTP), complete local binary pattern (CLBP) for the task of recognition in infrared images has been evaluated. The performance of the method is validated with target clips obtained from 'CSIR-CSIO moving object thermal infrared imagery dataset'. The number of classes is four-three different target classes (Ambassador, Auto and Pedestrian) and one class representing the background. Classification accuracies of 89.48 %, 100 % and 100 % were obtained for LBP, LTP and CLBP, respectively. The results indicate the suitability of LBP operator for target recognition in infrared images.

关键词： Local binary pattern Local ternary pattern Complete local binary pattern Infrared Recognition Support vector machine

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