Deployment of Deep Learning Models on Resource-Deficient Devices for Object Detection
Ameema Zainab, Dabeeruddin Syed · 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · 2020
Detection of images or objects in motion have been highly worked upon, and the detection has been incorporated, required and utilized in applications. A few of the limitations include the lack of computational resources, lack of strategic and methodological data analysis of the observed trained data. The accuracy of detection is dependent on movement, velocity of the objects and Illuminacy. Therefore, it is required that new techniques and strategies of detection are drafted, applied and recognized. In this work, we have worked upon a model based on scalable object detection, using Deep Neural Networks to localize and track people, cars, potted plants and other categories in the camera preview in real-time. The trained model from google inception model has been implemented in an android application. This integrated application works real-time and can be used handy in a mobile phone or any other smart device with minimal computational resources.