Complexity and accuracy of hand-crafted detection methods compared to convolutional neural networks

Abstract

Even though Convolutional Neural Networks have had the best accuracy in the last few years, they have a price in term of computational complexity and memory footprint, due to a large number of multiply-accumulate operations and model parameters. For embedded systems, this complexity severely limits the opportunities to reduce power consumption, which is dominated by memory read and write operations. Anticipating the oncoming integration into intelligent sensor devices, we compare hand-crafted features for the detection of a limited number of objects against some typical convolutional neural network architectures. Experiments on some state-of-the-art datasets, addressing detection tasks, show that for some problems the increased complexity of neural networks is not reflected by a large increase in accuracy. Moreover, our analysis suggests that for embedded devices hand-crafted features are still competitive in terms of accuracy/complexity trade-offs.