Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

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This work details the application of a metal oxide semiconductor (MOS) sensor based electronic nose (e-nose) system in the discrimination of lung cancer and chronic obstructive pulmonary disease (COPD) from healthy controls. The sensor array integrated with supervised classification algorithms was able to detect and classify exhaled breath samples from healthy controls, patients with COPD, and lung cancer by recognizing the amount of volatile organic compounds present in it. This paper details the e-nose design, participant selection, sampling methods, and data analysis. The clinical feasibility of the system was checked in 32 lung cancer patients, 38 COPD patients, and 72 healthy controls including smokers and non-smokers. One of the advantages of the equipment design was portability and robustness since the system was conditioned with elements that allowed its easy movement. In the discrimination of lung cancer from controls, the k-nearest neighbors gave an acceptable accuracy, sensitivity, and specificity of 91.3%, 84.4%, and 94.4% respectively. The support vector machine gave better results for COPD discrimination from controls with 90.9% accuracy, 81.6% sensitivity, and 95.8% specificity. Even though the attained results were good, further examinations are essential to enhance the sensor array system, investigate the long-run reproducibility, repeatability, and enlarge its relevancy.

Purpose: The aim of this paper was to discuss the design and development of an innovative e-nose system which can detect respiratory ailments by detecting the Volatile Organic Compounds (VOCs) in the expelled breath. In addition to nitrogen, oxygen, and carbon dioxide, the expelled breath contains several VOCs, some of which are indicative of lung-related conditions and can differentiate healthy controls from people affected with pulmonary diseases. Methods: This work detailed the sensor selection process, the assembly of the sensors into a sensor array, the design and implementation of the circuit, sampling methods, and an algorithm for data analysis. The clinical feasibility of the system was checked in 27 lung cancer patients, 22 chronic obstructive pulmonary disease (COPD) patients, and 39 healthy controls including smokers and non-smokers. Results: The classification model developed using the support vector machine (SVM) was able to provide accuracy, sensitivity, and specificity of 88.79, 89.58 and 88.23%, respectively for lung cancer, and 78.70, 72.50 and 82.35%, respectively for COPD. Conclusions: The sensor array system developed with TGS gas sensors was non-invasive, low cost, and gave a rapid response. It has been demonstrated that the VOC profiles of patients with pulmonary diseases and healthy controls are different, hence, the e-nose system can be used as a potential diagnostic device for patients with lung diseases.

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