Browsing by Author "Rajkumar Thanasekaran"
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Publication Machine Learning Approach Regarding The Classification And Prediction Of Dog Sounds: A Case Study Of South Indian Breeds(MDPI, 2022) ;Prabu Mohandas ;Jerline Sheebha Anni ;Khairunnisa Hasikin ;Dhayalan Velauthapillai ;Veena Raj ;Thanihaichelvan Murugathas ;Muhammad Mokhzaini AzizanRajkumar ThanasekaranBarking is a form of vocal communication made by dogs. Each type of bark made by dogs has a distinct context. The classification of dog bark pattern will aid in the understanding of barking action. In this study, a machine learning algorithm is used to analyze the pattern of barking from two different dog species: Rajapalayam Hound and Kombai Hound. The objective is to find the context of the dog barking pattern based on various real-time scenarios, including whether the dogs are alone, looking at strangers, or showing an eagerness to fight. The barks of the dogs were recorded inside the house under different scenarios, such as while identifying the owner or strangers. Machine learning algorithms, such as the reinforcement learning method, were used in predicting and classifying the dog sounds. Q-learning is a reinforcement learning that will generate the next best action for the given state. It is a model-free learning used to find the best course of dog action for the given current state of the dog. The Q-learning algorithm had been used in improving the prediction of dog sounds by updating the values of learning, where the values with the highest reward were taken into consideration. In total, 6171 barks were collected from the dogs chosen for study, and the proposed approach achieved a correct prediction accuracy of 85.19% of the dog sounds. - Some of the metrics are blocked by yourconsent settings
Publication Object Detection and Movement Tracking Using Tubelets and Faster RCNN Algorithm with Anchor Generation(Hindawi Limited, 2021) ;Prabu Mohandas ;Jerline Sheebha Anni ;Rajkumar Thanasekaran ;Khairunnisa HasikinMuhammad Mokhzaini AzizanObject detection in images and videos has become an important task in computer vision. It has been a challenging task due to misclassification and localization errors. The proposed approach explored the feasibility of automated detection and tracking of elephant intrusion along forest border areas. Due to an alarming increase in crop damages resulted from movements of elephant herds, combined with high risk of elephant extinction due to human activities, this paper looked into an efficient solution through elephant’s tracking. The convolutional neural network with transfer learning is used as the model for object classification and feature extraction. A new tracking system using automated tubelet generation and anchor generation methods in combination with faster RCNN was developed and tested on 5,482 video sequences. Real-time video taken for analysis consisted of heavily occluded objects such as trees and animals. Tubelet generated from each video sequence with intersection over union (IoU) thresholds have been effective in tracking the elephant object movement in the forest areas. The proposed work has been compared with other state-of-the-art techniques, namely, faster RCNN, YOLO v3, and HyperNet. Experimental results on the real-time dataset show that the proposed work achieves an improved performance of 73.9% in detecting and tracking of objects, which outperformed the existing approaches.