Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Ashwin, T.S.Subject: search.filters.subject.Kinect

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    Kinect Based Suspicious Posture Recognition for Real-Time Home Security Applications
    (Institute of Electrical and Electronics Engineers Inc., 2018) Vikram, M.; Anantharaman, A.; Suhas, B.S.; Ashwin, T.S.; Guddeti, R.M.R.
    Suspicious posture recognition is a paramount task with numerous applications in everyday life. We explore one such application in real-time home security using the Microsoft Kinect depth camera. We propose a novel method where the remote device itself detects the suspicious activity. The server is alerted by the remote device in case of a suspicious activity which further alerts the home owners immediately. We show that our method, works in real-time, is robust towards changing lighting conditions and the computations happen on the remote device itself which makes it suitable for real-time home security. © 2018 IEEE.
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    Optimized Object Detection Technique in Video Surveillance System Using Depth Images
    (Springer, 2020) Shahzad Alam, M.; Ashwin, T.S.; Guddeti, R.M.R.
    In real-time surveillance and intrusion detection, it is difficult to rely only on RGB image-based videos as the accuracy of detected object is low in the low light condition and if the video surveillance area is completely dark then the object will not be detected. Hence, in this paper, we propose a method which can increase the accuracy of object detection even in low light conditions. This paper also shows how the light intensity affects the probability of object detection in RGB, depth, and infrared images. The depth information is obtained from Kinect sensor and YOLO architecture is used to detect the object in real-time. We experimented the proposed method using real-time surveillance system which gave very promising results when applied on depth images which were taken in low light conditions. Further, in real-time object detection, we cannot apply object detection technique before applying any image preprocessing. So we investigated the depth image by which the accuracy of object detection can be improved without applying any image preprocessing. Experimental results demonstrated that depth image (96%) outperforms RGB image (48%) and infrared image (54%) in extreme low light conditions. © 2020, Springer Nature Singapore Pte Ltd.