# PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology

arXiv: https://arxiv.org/abs/2106.11902 · HTML: https://mlsanity.com/paper/2106.11902/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 50 references that are in the map.
- First posted on arXiv 22 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Scaling Egocentric Vision: The EPIC-KITCHENS Dataset](https://mlsanity.com/paper/1804.02748/) (2018)
- [Temporal Relational Reasoning in Videos](https://mlsanity.com/paper/1711.08496/) (2017)
- [A DIRT-T Approach to Unsupervised Domain Adaptation](https://mlsanity.com/paper/1802.08735/) (2018)
- [Adversarial Dropout Regularization](https://mlsanity.com/paper/1711.01575/) (2017)
- [Actor and Observer: Joint Modeling of First and Third-Person Videos](https://mlsanity.com/paper/1804.09627/) (2018)
- [Temporal Attentive Alignment for Large-Scale Video Domain Adaptation](https://mlsanity.com/paper/1907.12743/) (2019)
- [Adversarial Cross-Domain Action Recognition with Co-Attention](https://mlsanity.com/paper/1912.10405/) (2019)
- [Associative Domain Adaptation](https://mlsanity.com/paper/1708.00938/) (2017)
- [Unsupervised Learning of View-invariant Action Representations](https://mlsanity.com/paper/1809.01844/) (2018)

## Built on by (most cited first, 5 of 5)

- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction](https://mlsanity.com/paper/2512.12378/) (2025)
- [Talk is Not Always Cheap: Promoting Wireless Sensing Models with Text Prompts](https://mlsanity.com/paper/2504.14621/) (2025)
- [Towards Multi-User Activity Recognition through Facilitated Training Data and Deep Learning for Human-Robot Collaboration Applications](https://mlsanity.com/paper/2302.05763/) (2023)
- [Exploring Spatial-Temporal Representation via Star Graph for mmWave Radar-based Human Activity Recognition](https://mlsanity.com/paper/2512.12013/) (2025)

## Abstract (arXiv)

With the advancement of deep neural networks and computer vision-based Human Activity Recognition, employment of Point-Cloud Data technologies (LiDAR, mmWave) has seen a lot interests due to its privacy preserving nature. Given the high promise of accurate PCD technologies, we develop, PALMAR, a multiple-inhabitant activity recognition system by employing efficient signal processing and novel machine learning techniques to track individual person towards developing an adaptive multi-inhabitant tracking and HAR system. More specifically, we propose (i) a voxelized feature representation-based real-time PCD fine-tuning method, (ii) efficient clustering (DBSCAN and BIRCH), Adaptive Order Hidden Markov Model based multi-person tracking and crossover ambiguity reduction techniques and (iii) novel adaptive deep learning-based domain adaptation technique to improve the accuracy of HAR in presence of data scarcity and diversity (device, location and population diversity). We experimentally evaluate our framework and systems using (i) a real-time PCD collected by three devices (3D LiDAR and 79 GHz mmWave) from 6 participants, (ii) one publicly available 3D LiDAR activity data (28 participants) and (iii) an embedded hardware prototype system which provided promising HAR performances in multi-inhabitants (96%) scenario with a 63% improvement of multi-person tracking than state-of-art framework without losing significant system performances in the edge computing device.