Live demonstration: Real-time implementation of proto-object based visual saliency model

We will demonstrate a real-time implementation of a pro-toobject based neuromorphic visual saliency model [1] on an embedded processing board. Visual saliency models are difficult to implement in hardware for real-time applications due to their computational complexity. The conventional implementation is not optimal because of the requirement of a large number of convolution operations for filtering on several feature channels across multiple image pyramids. Our current implementation considers the dynamic temporal motion change by convoluting along time efficiently by parallelly processing them. We have implemented the model on an NVIDIA Jetson TX1 board (Fig. 1), which has NVIDIA Maxwell GPU with 256 NVIDIA CUDA Cores, hosted on an Ubuntu environment. The board has a 5 MP fixed focus MIPI CSI camera through which the frames are fetched using a Quad-core ARM Cortex-A57 MPCore Processor with 4 GB LPDDR4 Memory. The camera module fetches the frames to the application for processing, and the result is then displayed through the HDMI port. The application is written in Tensorflow, Cuda, and Python and uses several Python libraries. For further analysis, the user can also save the output onto a file.