Object-Based Surveillance Video Synopsis Using Genetic Algorithm

Video synopsis provides representation of the long surveillance video, while preserving the essential activities of the original video. The activity in the original video is covered into a shorter period by simultaneously displaying multiple activities, which originally occurred at different time segments. As activities are to be displayed in different time segments than original video, the process begins with extracting moving objects. Temporal median algorithm is used to model background and foreground objects are detected using background subtraction method. Each moving object is represented as a space-time activity tube in the video. The concept of genetic algorithm is used for optimized temporal shifting of activity tubes. The temporal arrangement of tubes which results in minimum collision and maintains chronological order of events is considered as the best solution. The time-lapse background video is generated next, which is used as background for the synopsis video. Finally, the activity tubes are stitched on the time-lapse background video using Poisson image editing.

Moving Object Classification in a Video Sequence

Now-a-day's monitoring objects in a video is a major issue in areas such as airports, banks, military installations. Object identification and recognition are the two important tasks in such areas. These require scanning the entire video which is a time consuming process and hence requires a Robust method to detect and classify the objects. Outdoor environments are more challenging because of occlusion and large distance between camera and moving objects. Existing classification methods have proven to have set of limitations under different conditions. In the proposed system, video is divided into frames and Color features using RGB, HSV histograms, Structure features using HoG, DHoG, Harris, Prewitt, LoG operators and Texture features using LBP, Fourier and Wavelet transforms are extracted. Additionally BoV is used for improving the classification performance. Test results proved that SVM classifier works better compared to Bagging, Boosting, J48 classifiers and works well in outdoor environments.

Measures of Image and Video Segmentation

Study on Video and Image segmentation is currently limited by the lack of evaluation metrics and benchmark datasets that covers the large variety of sub-problems appearing in image and video segmentation. Proposed chapter provides an analysis of Evaluation Metrics, Datasets for Image and Video Segmentation methods. Importance is on wide-ranging, Datasets robust Metrics which used for evaluation purposes without inducing any bias towards the evaluation results. Introductory Section discusses traditional image and video segmentation methods available, the importance and need of measures, metrics and dataset required to evaluate segmentation algorithms are discussed in next section. Main focus of the chapter explains the measures, metrics and dataset available for evaluation of segmentation techniques of both image and video. The goal is to provide details about a set of impartial datasets and evaluation metrics and to leave the final evaluation of the evaluation process to the understanding of the reader.

Reducing False Alarms in Vision-Based Fire Detection

Computer vision techniques are mainly used now a days to detect the fire. There are also many challenges in trying whether the region detected as fire is actually a fire this is perhaps mainly because the color of fire can range from red yellow to almost white. So fire region cannot be detected only by a single feature and many other features (i.e.) color have to be taken into consideration. Early warning and instantaneous responses are the preventing ideas to avoid losses affecting environment as well as human causalities. Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms. In order to reduce false alarms of conventional fire detection systems, system make use of vision based fire detection system. This chapter discuss about the fundamentals of videos, various issues in processing video signals, various algorithms for video processing using vision techniques.

Technical Evaluation, Development, and Implementation of a Remote Monitoring System for a Golf Cart

A technical evaluation of the sensing, communication and software system for the development and implementation of a remote monitoring system for an electric golf cart is presented. According to the vehicle's characteristics and the user's needs, the technical and economic aspects are combined in the best possible way, thereby implementing its monitoring at a distance. The monitoring system is used in two important stages: teleoperation and the vehicle complete autonomy. This allows the acquisition of video images on the vehicle, which are sent wirelessly to the monitoring station, where they are presented through a user-friendly interface. With the purpose of complementing the information sent to the remote user of the vehicle, several important teleoperation variables of a land vehicle, such as voltage level, current and speed are sensed.

Study of Various Image Segmentation Methodologies

In day-to-day life, new technologies are emerging in the field of Image processing, especially in the domain of segmentation. Image segmentation is the most important part in digital image processing. Segmentation is nothing but a portion of any image and object. In image segmentation, the digital image is divided into multiple set of pixels. Image segmentation is generally required to cut out region of interest (ROI) from an image. Currently there are many different algorithms available for image segmentation. This chapter presents a brief outline of some of the most common segmentation techniques (e.g. Segmentation based on thresholding, Model based segmentation, Segmentation based on edge detection, Segmentation based on clustering, etc.,) mentioning its advantages as well as the drawbacks. The Matlab simulated results of different available image segmentation techniques are also given for better understanding of image segmentation. Simply, different image segmentation algorithms with their prospects are reviewed in this chapter to reduce the time of literature survey of the future researchers.

An Overview of Steganography

Nowadays, due to the increasing need for providing secrecy in an open environment such as the internet, data hiding has been widely used. Steganography is one of the most important data hiding techniques which hides the existence of the secret message in cover files or carriers such as video, images, audio or text files. In this chapter; steganography will be introduced, some historical events will be listed, steganography system requirements, categories, classifications, cover files will be discussed focusing on image and video files, steganography system evaluation, attacks, applications will be explained in details and finally last section concludes the chapter.

Image Registration Techniques and Frameworks

Image registration allude to transforming one image with reference to another (geometrically alignment of reference and sensed images) i.e. the process of overlaying images of the same scene, seized by assorted sensors, from different viewpoints at variant time. Virtually all large image evaluating or mining systems require image registration, as an intermediate step. Over the years, a broad range of techniques has been flourished for various types of data and problems. These approaches are classified according to their nature mainly as area-based and feature-based and on four basic tread of image registration procedure namely feature detection, feature matching, mapping function design, and image transformation and resampling. The current chapter highlights the cogitation effect of four different registration techniques, namely Affine transformation based registration, Rigid transformation based registration, B-splines registration, and Demons registration. It provides a comparative study among all of these registration techniques as well as different frameworks involved in registration process.

Design of Reconfigurable Architectures for Steganography System

The most crucial task in real-time processing of image or video steganography algorithms is to reduce the computational delay and increase the throughput of a steganography embedding and extraction system. This problem is effectively addressed by implementing steganography hiding and extraction methods in reconfigurable hardware. This chapter presents a new high-speed reconfigurable architectures that have been designed for Least Significant Bit (LSB) and multi-bit based image steganography algorithm that suits Field Programmable Gate Arrays (FPGAs) or Application Specific Integrated Circuits (ASIC) implementation. Typical architectures of LSB steganography comprises secret message length finder, message hider, extractor, etc. The architectures may be realized either by using traditional hardware description languages (HDL) such as VHDL or Verilog. The designed architectures are synthesizable in FPGAs since the modules are RTL compliant. Before the FPGA/ASIC implementation, it is convenient to validate the steganography system in software to verify the concepts intended to implement.

Automated System for Crops Recognition and Classification

Marketing profit optimization and preventing the crops' infections are a critical issue. This requires crops recognition and classification based on their characteristics and different features. The current work proposed a recognition/classification system that applied to differentiate between fresh (healthy) from rotten crops as well as to identify each crop from the other based on their common feature vectors. Consequently, image processing is employed to perform the statistical measurements of each crop. ImageJ software was employed to analyze the desired crops to extract their features. These extracted features are used for further crops recognition and classification using the Least Mean Square Error (LMSE) algorithm in Matlab. Another classification method based on Bag of Features (BoF) technique is employed to classify crops into classes, namely healthy and rotten. The experimental results are applied of databases for orange, mango, tomato and potatoes. The achieved recognition (classification) rate by using the LMSE for all datasets (healthy and rotten) has 100%. However, after adding 10%, 20%, and 30% Gaussian noise, the obtained the average recognition rates were 85%, 70%, and 25%; respectively. Moreover, the classification (healthy and rotten) using BoF achieved accuracies of 100%, 88%, 94%, and 75% for potatoes, mango, orange, and tomato; respectively. Furthermore, the classification for all the healthy datasets achieved accuracy of 88%.