Browsing by Author "Shahzad Alam, M."

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    Cost-effective real-time aerial surveillance system using edge computing
    (Springer, 2020) Shahzad Alam, M.; Gupta, S.K.
    Nowadays there is an emerging need for surveillance in order to maintain the public places more secure and ensure the safety and security of the people. Many government agencies require some autonomous system for surveillance of the large areas which can give them precise and real-time information like number of vehicles, people, and other objects. An aerial surveillance system will be very effective in this scenario and platform like Unmanned Aerial vehicle (UAV) will be very reliable and cost-effective option for this task. To make the system fully autonomous, we require real-time object detection that is computationally complex and time consuming due to the heavy load on the limited processing and payload capacity of low-cost UAV. In this paper, we propose a cost-effective approach for aerial surveillance in which we move the heavy computation tasks to the cloud while keeping limited computation on-board of UAV system using Edge computing technique. Further this will maintain the minimum communication between UAV and the cloud thus proposed system will reduce the network traffic and also delay. Proposed system is based on the state-of-art technique YOLO (You Look Only Once) for real time object detection. © Springer Nature Switzerland AG 2020.
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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.