Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Frontal Gait Recognition based on Hierarchical Centroid Shape Descriptor and Similarity Measurement(Institute of Electrical and Electronics Engineers Inc., 2019) Anusha, R.; Jaidhar, C.D.Gait recognition is an expanding stream in biometrics, intended to recognize individuals through the investigation of their walking pattern. This pattern is obtained from a distance, without the active participation of the people. One of the difficulties of the appearance-based gait approach is to enhance the performance of frontal gait recognition, as it carries less spatial and temporal data when compared with other view variations. As a result, to increase the performance of the frontal gait recognition, this paper presents a method which uses two-step procedure; the Hierarchical centroid Shape descriptor (HCSD) and the similarity measurement. The proposed method was assessed on the broadly used CASIA A, CASIA B, and CMU MoBo gait databases. The experimental outcomes showed that the proposed method gave promising results and outperforms certain state-of-the-art methods in terms of recognition performance. © 2019 IEEE.Item An Approach to Speed Invariant Gait Analysis for Human Recognition using Mutual Information(Institute of Electrical and Electronics Engineers Inc., 2019) Anusha, R.; Jaidhar, C.D.Gait is a biometric characteristic that facilitates the identification of individuals with low-resolution images. This aspect intensifies its utility in many human detection applications. However, there are many challenges that adversely affect the gait recognition performance. They are caused by the impact of various covariate aspects such as, changes in clothing and carrying conditions, walking speed, walking surface conditions, view variations, and so on. This paper proposes an effective approach for speed-invariant gait recognition system. This approach uses the Region of Interest (ROI) extracted from Gait Energy Image (GEI) to classify a probe sample into a gallery sample. The mutual information obtained from a probe and gallery sample, followed by their classification capture the spatial dynamics of GEI efficiently to improve the gait recognition performance. Further, the proposed method is evaluated on CASIA C and OU-ISIR Treadmill A gait databases. Experimental results demonstrate the capability of the proposed approach in comparison with the existing gait recognition methods. © 2019 IEEE.Item On Human Identification Using Running Patterns: A Straightforward Approach(Springer Verlag service@springer.de, 2020) Anusha, R.; Jaidhar, C.D.Gait is a promising biometric for which various methods have been developed to recognize individuals by the pattern of their walking. Nevertheless, the possibility of identifying individuals by using their running video remains largely unexplored. This paper proposes a new and simple method that extends the feature based approach to recognize people by the way they run. In this work, 12 features were extracted from each image of a gait cycle. These are statistical, texture based and area based features. The Relief feature selection method is employed to select the most relevant features. These selected features are classified using k-NN (k-Nearest Neighbor) classifier. The experiments are carried out on KTH and Weizmann database. The obtained experimental results demonstrate the efficiency of the proposed method. © 2020, Springer Nature Switzerland AG.Item Gaussian Filtered Gait Energy Template and Centroid Corner Distance Features for Human Gait Recognition(Institute of Electrical and Electronics Engineers Inc., 2019) Anusha, R.; Jaidhar, C.D.One of the convincing and latest biometric systems is gait recognition because of its ability to unobtrusively identify an individual at a distance and with low-resolution images. This study proposes an efficient method to enhance the performance of the gait detection system. The gait silhouette images are initially processed with two gait portrayal methods as the feature resources: Gait Energy Image (GEI) and Gaussian Filtered-Gait Energy Image (GF-GEI). Further, an effort has been made to present a statistical shape examination method, which is established on GF-GEI, and it is divided into six independent horizontal segments. The centroid corner distance features obtained from these horizontal segments forms the feature vector of the image. The proposed method is assessed on the widely used CASIA A, CASIA B, and OU-ISIR D gait datasets. The empirical results illustrate that the performance of the proposed approach is promising and surpasses some state-of-the-art gait identification methods recorded in literature. © 2019 IEEE.Item Classification of Soil Fertility using Machine Learning-based Classifier(Institute of Electrical and Electronics Engineers Inc., 2021) Sujatha, M.; Jaidhar, C.D.Indian economy depends on agriculture production. However, the quantity of agricultural production depends on fertility of the soil. In this study chemical soil measurements are used to classify fertility of the soil. The 11 soil parameters namely, pH, EC, OC, P, K, S, Zn, B, Fe, Cu, Mn were used to classify soil as LOW, MEDIUM and HIGH fertile. The machine learning-based classifiers such as naive bayes, logistic regression, Support Vector Machine (SVM), decision tree bagging, Boosted Regression Tree (BRT), Random Forests (RF) were used to classify the soil as LOW, MEDIUM and HIGH fertile soil. The RF classifier showed best performance among other classifiers. © 2021 IEEE.Item CNN-based Soil Fertility Classification with Fertilizer Prescription(Institute of Electrical and Electronics Engineers Inc., 2023) Sujatha, M.; Jaidhar, C.D.Soil fertility plays a vital role in crop growth, and thus, the rapid acquisition of soil fertility levels and applying precise fertilizer is significant for sustainable agricultural development. However, obtaining accurate soil fertility estimates proves difficult due to the traditional practice of laboratory analysis of soil samples. This study proposes fertilizer prescriptions based on the Convolutional Neural Networks (CNNs) classifier results. The soil fertility is classified as HIGH, MEDIUM, or LOW fertile based on the chemical measurements of soil parameters, including EC, pH, OC, P, K, S, Zn, B, Cu, Fe, and Mn. The experiments were carried out by varying kernel size from $3\times 3$ to $7\times 7$ and input grid size from $11\times 11$ to $13\times 13$. The proposed approach outperformed with an Accuracy of 97.24% without oversampling the dataset for kernel size $3\times 3$ and input grid size $11\times 11$. Further, for the dataset oversampled using Synthetic Minority Oversampling (SMOTE) technique, the proposed approach achieved the highest Accuracy of 97.52% for kernel size $3\times 3$ and input grid size $12\times 12$. The study helps in the precise application of fertilizers for specific crops based on classification results. © 2023 IEEE.Item Micro-Moment Classification for Anomalous Power Consumption Detection using 1D CNN(Institute of Electrical and Electronics Engineers Inc., 2023) Nayak, R.; Jaidhar, C.D.Identifying anomalous power consumption is essential in improving energy efficiency in buildings. With the help of sensors and other intelligent systems installed in buildings (including smart homes), identifying anomalous power consumption becomes easy. In this work, 1 Dimensional Convolutional Neural Network (1D CNN)-based classification model is proposed to classify the micro-moments to identify the anomalous power consumption in the presence and absence of the consumer. The SimDataset values are normalized, and each instance with ten features is given as input to the 1D CNN. The robustness of the proposed model is defined by experimenting with varying the hyperparameter to obtain the best performance in the standard performance evaluation metrics. The results depicted that the suggested model outperformed the state-of-the-art, producing an accuracy of 96.4% and a weighted average F1-score of 0.962. © 2023 IEEE.Item Symbolic Deterministic Finite Automata-based Automated Fertilizer Prescription(Institute of Electrical and Electronics Engineers Inc., 2023) Sujatha, M.; Jaidhar, C.D.Sustainable agriculture requires the use of an adequate amount of fertilizers. In this research work, initially, an attempt was made to classify soil fertility using machine learning-based classifiers. To overcome the drawbacks of machine learning-based classifiers, this research uses Symbolic Deterministic Finite Automata (SDFA) for soil fertility classification. The proposed method classifies soil fertility as LOW, MEDIUM (MED), or HIGH using the levels of four soil parameters, including pH, Electrical Conductivity (EC), Organic Carbon (OC), and Nitrogen (N). The proposed approach was assessed using Sentinel-2 remotely sensed data and laboratory-measured soil-health data. The experiments' outcomes show that the proposed approach effectively classifies soil fertility. The accuracy achieved using Sentinel-2 data was 100%, while the accuracy gained using laboratory-measured data with four and twelve soil parameters were 100% and 98.37%, respectively. The results of soil fertility classification were used to recommend fertilizers. © 2023 IEEE.Item Experimental Study on Impact of Appliance ID-Based Normalization on SimDataset for Anomalous Power Consumption Classification(Institute of Electrical and Electronics Engineers Inc., 2024) Nayak, R.; Jaidhar, C.D.In terms of annual worldwide energy consumption, buildings use more energy than any other sector. Enhancing buildings' energy efficiency and ensuring security of the appliances requires iden-tifying abnormal power usage. Identifying anomalous power usage is essential for energy conservation. This study suggests an experimental analysis of SimDataset used for detecting micro-moment-based abnormal power usage. Five machine learning-based classifiers-Random Forest (RF), Support Vector Ma-chine (SVM), K Nearest Neighbors (KNN), Naive Bayes (NB), and Decision Tree (DT)-are used to detect unusual consumption of electricity. The Sim-Dataset has undergone binary and multi-class classi-fication. Effect on the performance of the classifiers after the inclusion of new features is examined. Computational complexity of the classifiers is also analyzed. Experimental results showed, the binary and multi-class classification using the RF model with the original dataset, with Min-Max Normalized Power feature and Appliance Id-based Normalized Power feature, produced identical and maximum accuracy, precision, recall, and F1-Score. © 2024 IEEE.