A DNN-based object detection system on mobile cloud computing
Buren Qi, Mengfei Wu, Lin Zhang · 2017
With the development of big data and the improvement of computing power, deep learning has made a very prominent breakthrough in computer vision. However, the computational overhead of the Deep Neural Network (DNN) for video processing in mobile devices is extremely high. To address the problem above, this paper combines smartphones with the cloud to realize a DNN-based object detection system. The main contributions are three-fold. (i) A model scheduling algorithm is proposed to adaptively select the operating environment (cloud or mobile) according to the conditions of network and mobile devices. (ii) To meet the hardware requirements of mobile devices, the compact model variants are trained and generated with a small loss of precision. (iii) To reduce the latency, the outputs of DNN models are used to process (add bounding boxes and annotations) the video directly. Test results for runtime and precision show that our system outperforms the state-of-the-art in both detection accuracy and running speed.