NeuroDFD: Towards Efficient Driver Face Detection with Neuromorphic Vision Sensor

Face detection is a fundamental task for various computer vision applications. With the rapid improvement of computing power, most face detection algorithms adopt complex deep neural net architectures to increase accuracy. However, such algorithms are difficult to be applied on the mobile platform with restrained computing resources. To this end, this paper introduces a novel neuromorphic vision based single-shot driver face detection method, named NeuroDFD to handle driver face detection problem. Different from traditional CMOS camera, the neuromorphic vision sensor captures pixel level brightness changes signal and output them asynchronously in the form of events. It has the characteristics of high dynamic range and low data redundancy, which helps to meet the challenges of in-cabin lighting conditions and limited computing resources of edge devices. Based on the unique output, we first construct event representation by discretizing the time domain and present light weight translation-invariant backbone to extract multi-scale features. Then, we propose shift FPN and shift context module to promote the spatial-temporal features extraction with limited computation cost. Extensive experiments prove that the NeuroDFD can achieve remarkable detection performance with high efficiency.