Running predictions on a live video feed using Deep Neural Network (DNN) model can be time consuming. It involves processing of the incoming stream, image segmentation and object detection. Each prediction takes additional compute time which has an impact on the real time execution of the application. Utilizing Intel® Distribution of OpenVINO™ toolkit, the pre-trained DNN model is optimized for maximum performance running on edge devices powered by Intel® processors (CPU/VPU/FPGA). The substantial improvement in inference time helps in accelerated processing of the various steps in the video processing pipeline. An Object detection model based on SSD InceptionV2 is optimized and run on edge devices which speeds up while retaining the mAP of the model. This optimized model can be deployed on low powered hardware like IOT devices, cameras running on Intel® hardware. Various industrial use cases like ADAS, Surveillance etc. can benefit from this.
Social distancing is an effective mechanism for Covid-19 infection control. It is an action taken to minimize contact with other individuals. It has been suggested that maintaining approximately 2 meters from another individual result in a marked reduction in transmission of most flu virus strains, including COVID-19. With the virus still spreading, it is important for everyone to adhere to social distancing norms. The aim of this innovative Social Distance Monitoring (SDM) Solution is to provide a tool for organizations to effectively re-open post lockdowns. The solution can detect and track whether social distancing norms are being followed as per the published guidelines. The Solution has advanced features like detecting violations over 2 seconds. This application is optimized with Intel® Distribution of OpenVINO™ Toolkit and can run inference algorithms across a variety of Intel enabled edge/data center devices.