Passive detection of footsteps in domestic settings can allow the development of assistive technologies that can monitor mobility patterns of older adults in their home environment. Acoustic footstep detection is a promising approach for non-intrusive detection of footsteps. So far there has been limited work in developing robust acoustic footstep detection systems that can operate in noisy home environments. In this paper, we propose a novel application of the Attention based Recurrent Deep Neural Network to detect human footsteps in noisy overlapping audio streams. The model is trained on synthetic data which simulates the acoustic scene in a home environment. To evaluate performance, we reproduced two footstep detection models from literature and compared them using the newly developed Polyphonic Sound Detection Scores (PSDS). Our model achieved the highest PSDS and is close to the highest score achieved by generic indoor AED models in DCASE. The proposed system is designed to both detect and track footsteps within a home setting, and to enhance state-of-the-art digital health-care solutions for empowering older adults to live autonomously in their own homes.