Fast an Robust Face Tracking for CNN chips: application to wheelchair driving

An algorithm for fast and robust face tracking with the CNN universal machine is proposed in this paper. It is applied to a driving mechanism for a wheelchair with an on-chip implementation. A novel object tracking CNN visual algorithm is introduced and employed in the tracking of multiple face features. The speed and robustness of this method are achieved due to the parallelism in the visual algorithm, and the tracking of multiple face features. The tracking algorithm is designed to achieve a high frame rate and exploit the specific properties of face features. The face tracking method proposed here was implemented on a Bi-I stand-alone cellular vision system and applied to a wheelchair driving mechanism. The template operations were trained and/or fine-tuned in order to generate chip-specific robust templates. In order to improve performance in environments with varying illumination, an adaptive image capture procedure was also introduced. Our simulations with a 3D model wheelchair showed that the final algorithm is capable of performing tracking with a frame rate of 92 frames/sec, which is supposedly enough for real-time driving in most of the real life situations