# Toward Smart Doors: A Position Paper

arXiv: https://arxiv.org/abs/2209.11770 · HTML: https://mlsanity.com/paper/2209.11770/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 65 references that are in the map.
- First posted on arXiv 23 Sep 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction](https://mlsanity.com/paper/2004.02025/) (2020)
- [Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2003.08111/) (2020)
- [From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting](https://mlsanity.com/paper/2012.01526/) (2020)
- [Repulsion Loss: Detecting Pedestrians in a Crowd](https://mlsanity.com/paper/1711.07752/) (2017)
- [Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data](https://mlsanity.com/paper/2001.03093/) (2020)
- [CrowdNet: A Deep Convolutional Network for Dense Crowd Counting](https://mlsanity.com/paper/1608.06197/) (2016)
- [DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation](https://mlsanity.com/paper/1712.06679/) (2017)
- [A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devices](https://mlsanity.com/paper/1608.08376/) (2016)
- [An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark](https://mlsanity.com/paper/1805.07663/) (2018)
- [Ten Years of Pedestrian Detection, What Have We Learned?](https://mlsanity.com/paper/1411.4304/) (2014)
- [Forecasting Pedestrian Trajectory with Machine-Annotated Training Data](https://mlsanity.com/paper/1905.03681/) (2019)
- [Under the Hood of Transformer Networks for Trajectory Forecasting](https://mlsanity.com/paper/2203.11878/) (2022)

## Built on by (most cited first, 2 of 2)

- [Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications](https://mlsanity.com/paper/2303.12524/) (2023)
- [SITUATE: Indoor Human Trajectory Prediction through Geometric Features and Self-Supervised Vision Representation](https://mlsanity.com/paper/2409.00774/) (2024)

## Abstract (arXiv)

Conventional automatic doors cannot distinguish between people wishing to pass through the door and people passing by the door, so they often open unnecessarily. This leads to the need to adopt new systems in both commercial and non-commercial environments: smart doors. In particular, a smart door system predicts the intention of people near the door based on the social context of the surrounding environment and then makes rational decisions about whether or not to open the door. This work proposes the first position paper related to smart doors, without bells and whistles. We first point out that the problem not only concerns reliability, climate control, safety, and mode of operation. Indeed, a system to predict the intention of people near the door also involves a deeper understanding of the social context of the scene through a complex combined analysis of proxemics and scene reasoning. Furthermore, we conduct an exhaustive literature review about automatic doors, providing a novel system formulation. Also, we present an analysis of the possible future application of smart doors, a description of the ethical shortcomings, and legislative issues.