Motion Detection and Face Recognition for CCTV Surveillance System

Closed Circuit Television (CCTV) is currently used in daily life for a variety purpose. Development of the use of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognation in CCTV video is done to be a base for decision making to produce automated, effective and efficient integrated system. This CCTV video processing provides three outputs, a motion detection information, a face detection information and a face identification information. Accumulative Differences Images (ADI) used  for motion detection, and Haar Classifiers Cascade used  for facial segmentation. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). The features was trained by Counter-Propagation Network (CPN). Offline tests performed on 45 CCTV video. The test results obtained a motion detection success rate of 92,655%, a face detection success rate of 76%, and a face detection success rate of 60%. The results concluded that the process of faces identification through CCTV video with natural background have not been able to obtain optimal results. The motion detection process is ideal to be applied to real-time conditions. But in combination with face recognition process, there is a significant delay time

A review of deep learning algorithms for computer vision systems in livestock.

In livestock operations, systematically monitoring animal body weight, bio-metric body measurements, animal behavior, feed bunk, and other difficult-to-measure phenotypes is manually unfeasible due to labor, costs, and animal stress. Applications of computer vision are growing in importance in livestock systems due to their ability to generate real-time, non-invasive, and accurate animal-level information. However, the development of a computer vision system requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves mas-sive datasets. This article aims to provide an overview of how deep learning has been implemented in computer vision systems used in livestock, and how such implementation can be an effective tool to predict animal phe-notypes and to accelerate the development of predictive modeling for precise management decisions. First, we reviewed the most recent milestones achieved with computer vision systems and its respective deep learning algorithms implemented in Animal Science studies. Second, we reviewed the published research studies in Animal Science, which used deep learning algorithms as the primary analytical strategy for image classification, object detection, object segmentation, and feature extraction. The great number of reviewed articles published in the last few years demonstrates the high interest and rapid development of deep learning algorithms in computer vision systems across livestock species. Deep learning algorithms for computer vision systems, such as Mask R-CNN, Faster R-CNN, YOLO (v3 and v4), DeepLab v3, U-Net and others have been used in Animal Science research studies. Additionally, network architectures such as ResNet, Inception, Xception, and VGG16 have been implemented in several studies across livestock species. The great performance of these deep learning algorithms suggests an33improved predictive ability in livestock applications and a faster inference.34However, only a few articles fully described the deep learning algorithms and its implementation. Thus, information regarding hyperparameter tuning, pre-trained weights, deep learning backbone, and hierarchical data structure were missed. We summarized peer-reviewed articles by computer vision tasks38(image classification, object detection, and object segmentation), deep learn-39ing algorithms, species, and phenotypes including animal identification and behavior, feed intake, animal body weight, and many others. Understanding the principles of computer vision and the algorithms used for each application is crucial to develop efficient systems in livestock operations. Such development will potentially have a major impact on the livestock industry by predicting real-time and accurate phenotypes, which could be used in the future to improve farm management decisions, breeding programs through high-throughput phenotyping, and optimized data-driven interventions

Odor Localization using Gas Sensor for Mobile Robot

This paper discusses the odor localization using Fuzzy logic algorithm. The concentrations of the source that is sensed by the gas sensors are used as the inputs of the fuzzy. The output of the Fuzzy logic is used to determine the PWM (Pulse Width Modulation) of driver motors of the robot. The path that the robot should track depends on the PWM of the right and left motors of the robot. When the concentration in the right side of the robot is higher than the middle and the left side, the fuzzy logic will give decision to the robot to move to the right. In that condition, the left motor is in the high speed condition and the right motor is in slow speed condition. Therefore, the robot will move to the right. The experiment was done in a conditioned room using a robot that is equipped with 3 gas sensors. Although the robot is still needed some improvements in accomplishing its task, the result shows that fuzzy algorithms are effective enough in performing odor localization task in mobile robot

RADAR Based Collision Avoidance for Unmanned Aircraft Systems

Unmanned Aircraft Systems (UAS) have become increasingly prevalent and will represent an increasing percentage of all aviation. These unmanned aircraft are available in a wide range of sizes and capabilities and can be used for a multitude of civilian and military applications. However, as the number of UAS increases so does the risk of mid-air collisions involving unmanned aircraft. This dissertation aims present one possible solution for addressing the mid-air collision problem in addition to increasing the levels of autonomy of UAS beyond waypoint navigation to include preemptive sensor-based collision avoidance. The presented research goes beyond the current state of the art by demonstrating the feasibility and providing an example of a scalable, self-contained, RADAR-based, collision avoidance system. The technology described herein can be made suitable for use on a miniature (Maximum Takeoff Weight \u3c 10kg) UAS platform. This is of paramount importance as the miniature UAS field has the lowest barriers to entry (acquisition and operating costs) and consequently represents the most rapidly increasing class of UAS

Honey Yield Prediction Using Tsukamoto Fuzzy Inference System

Honey is a natural product of bee. Since ancient times, honey has been known by humans as a source of natural food and also for traditional medicine. There are so many beneficial of honey, make people trying to do honeybee cultivate as a business solution to increase their income. However, to cultivate honey bees is not easy. Special knowledge is required on honey bee cultivation and capital is fairly large. In order for beekeepers not to lose from honey sales business, beekeepers should be able to estimate the honey yield accurately. Predicted yield of honey is used as a material consideration and help determine the decision in honey bee cultivation. This study provides a solution for prediction of honey yield type Apis Cerana with the main food of Calliandra flowers accurately. The method used in this research is Tsukamoto's fuzzy inference system (FIS) method. There are 3 input fuzzy used in this study, namely : Rainfall, number of box, and number of flower trees. The three fuzzy inputs are the determinants of the honey yield. The representation model used in the research is Trapezoid with fuzzy rules of 125 rules. While the test data in this research are rainfall and honey yield data for 21 years. The results of this study showed that the prediction of honey yield using FIS Tsukamoto closed the real honey yield with RMSE value of 9.44933860119277

Embedded Dsp Based License Plate Localization

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2008Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2008Bu çalışmada sözü edilen ve tasarlanan plaka yer saptama uygulaması sayısal sinyal işleme tabanlı gömülü bir mimari üzerinde gerçek zamanlı görüntü işleme kıstaslarına uyularak oluşturulan, ilgi duyulan bir konudur ve trafik yönetimi, gümrük kontrolleri, otoyol ödeme sistemleri, çalıntı arabaların tanınması, park yerleri, yasak bölgelerin kontrolü gibi birçok uygulama alanında gerek duyulan tam işlevli ve özdevimli tanıma sistemlerinin ayırt edici bir özelliğidir. Komple tanıma sisteminin ayırt edici bir parçası olmasının nedeni bir kez plaka yeri doğru olarak saptandığında aslında sorunun tanıma aşamasına indirgenmesidir. Tanıma biriminin girişinde yer alması ile başarımındaki kazancı iyileştirme sorununun ötesinde, bilinen bilgisayar tabanlı sistemlerle karşılaştırıldığında uygulamasının işlevine göre dar hacimli, kolay taşınabilir, düşük enerji tüketimli, ve düşük maliyetli mimariye sahip bir görüntü gözetim sisteminin parçası olması öncelikli bir durumdur. İşlemsel süreçlerde, sistem tasarım ve geliştirme aşamalarında tüm bu kısıtlamaların göz önünde tutulması amaçlanmıştır.Yer saptama işlemsel süreci genel olarak ayrıt bulma, eşikleme, bağlı bileşen etiketleme, plaka karakterlerini saran dikdörtgenlerin, ve son olarak da giriş görüntüsündeki plaka yerinin belirlenmesi aşamalarından oluşur. Öngörüldüğü şekliyle, tümleşik devrenin kullanışlı, yetkin, karmaşık, ve çok işlevli paralel çalışan komutları ile yüksek başarımlı doğrudan bellek erişimi, genel amaçlı giriş, çıkış ve çok çekirdekli yapısından faydalınarak plaka yer saptama işlemsel sürecinin geliştirilmesine ek olarak, sayısal işaret işleme tabanlı gömülü gerçek zamanlı bir görüntü izleme sistemi tipik bilgisayar tabanlı sistemlerle kıyaslandığında hem başarım hem verimlilik açısından gereksinimleri oldukça karşılayacak şekilde tasarlanmış ve geliştirilmiştir.The system presented and designed in this work as an embedded DSP architecture corresponding to real time video processing constraints is an application of license plate localization which is a challenging issue and distinctive unit of full featured and considerably standardized automated recognition systems required in several application areas like traffic management, custom controls, toll-pay systems, identification of stolen cars, parking, controlling of restricted zones. The reason of the fact it is a distinctive part of overall recognition system is that the issue is basically reduced to a recognition stage once the location of the license plate is correctly found. Beyond the reason that it is an issue to enhance the performance gain as a very important milestone prior to recognition modules, it is a priority task as a part of typical video surveillance system that the application should propose compact design, portability, low power consumption and low cost architecture as compared with generic personal computer based systems. It is aimed to consider all these constraints in algorithm and system design and development. Localization Algorithm generally consists of edge detection, threshold, component labeling, determination of surrounding rectangles of plate characters candidates, and finally localization of the plate in an input image. As contemplated, a DSP based embedded real-time video surveillance system is designed and developed comparatively sufficient to generic computer based systems in resolutions of both performance and efficiency constraints in addition to license plate localization algorithm development by utilizing flexible, powerful, complex multifunction instructions, high performance direct memory access and general purpose input outputs and multi core structures of integrated DSP.Yüksek LisansM.Sc

Pattern identification of biomedical images with time series: contrasting THz pulse imaging with DCE-MRIs

Objective We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. Methods Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. Validation Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. Results Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. Conclusion The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community