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Object detection gives a computer ability to classify certain objects in an image or video. However, to get good performance speed, the device’s specifications running object detection should be appropriately high. To enable device with low specification perform better, one way is offloading the computation process from a device with a low specification to another device with better specifications. This paper investigates the performance of object detection strategies on all-in-one Android mobile phone computation versus Android mobile phone computation with computational offloading on Nvidia Jetson Nano. The experiment carries out the video surveillance from the Android mobile phone with two scenarios, which are all-in-one object detection computation in a single Android device and decoupled object detection computation between an Android device and an Nvidia Jetson Nano. The video is sent by the Android application as an input for object detection using RTSP/RTMP streaming protocol and received by Nvidia Jetson Nano which acts as an RTSP/RTMP server. Then, the output of object detection is sent back to the Android device for being displayed to the user. The results show that the android devices Huawei Y7 Pro who has an average FPS performance of 1.98 and an average computing speed of 180 ms improves significantly when working with the Nvidia Jetson Nano, the average FPS become 10 and average computing speed become 95 ms. It means decoupling object detection computation between an Android device and an Nvidia Jetson Nano using the system provided in this paper successfully improve the detection speed performance.
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