Abstract: Methods, systems, and apparatuses for heatmap regression for neural networks are provided, including for systems with restricted computational resources, such as embedded systems. An exemplary method may include utilizing a trained neural network having a plurality of pooling layers, a plurality of convolutional layers, and an output layer to generate a heatmap. The plurality of pooling layers may reduce the spatial resolution of an input image, though the resolution may not be reduced below the spatial resolution of the output layer. The plurality of convolutional layers may utilize a plurality of depthwise convolutions and pointwise convolutions that will be stacked to provide a plurality of bottlenecks.

Abstract: Methods, systems, and apparatuses for heatmap regression for neural networks are provided, including for systems with restricted computational resources, such as embedded systems. An exemplary method may include utilizing a trained neural network having a plurality of pooling layers, a plurality of convolutional layers, and an output layer to generate a heatmap. The plurality of pooling layers may reduce the spatial resolution of an input image, though the resolution may not be reduced below the spatial resolution of the output layer. The plurality of convolutional layers may utilize a plurality of depthwise convolutions and pointwise convolutions that will be stacked to provide a plurality of bottlenecks.