Real Time Object Detection Can be Embedded on Low Powered Devices
DOI:
https://doi.org/10.26438/ijcse/v7i2.10051009Keywords:
TensorFlow, MobileNet, MS COCO, Real-time, and Object detectionAbstract
It has been a real challenge for computers with low computing power and memory to detect objects in real time. After the invention of Convolution Neural Networks (CNN) it is easy for computers to detect images and recognize them. There are several technologies and models which can detect objects in real time, but most of them require high end technologies in terms of GPUs and TPUs. Though, recently many new algorithms and models have been proposed, which runs on low resources. In this paper we studied MobileNets to detect objects using webcam to successfully build a real time object detection system. We observed the pre trained model of the famous MS COCO dataset to achieve our purpose. Moreover, we applied Google’s open source TensorFlow as our back end. This real time object detection system may help in future to solve various complex vision problems.
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