Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention

Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean F1 score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition.Comment: 8 pages. published in The International Symposium on Wearable Computers (ISWC) 201

Uncertainty-aware Gait Recognition via Learning from Dirichlet Distribution-based Evidence

Existing gait recognition frameworks retrieve an identity in the gallery based on the distance between a probe sample and the identities in the gallery. However, existing methods often neglect that the gallery may not contain identities corresponding to the probes, leading to recognition errors rather than raising an alarm. In this paper, we introduce a novel uncertainty-aware gait recognition method that models the uncertainty of identification based on learned evidence. Specifically, we treat our recognition model as an evidence collector to gather evidence from input samples and parameterize a Dirichlet distribution over the evidence. The Dirichlet distribution essentially represents the density of the probability assigned to the input samples. We utilize the distribution to evaluate the resultant uncertainty of each probe sample and then determine whether a probe has a counterpart in the gallery or not. To the best of our knowledge, our method is the first attempt to tackle gait recognition with uncertainty modelling. Moreover, our uncertain modeling significantly improves the robustness against out-of-distribution (OOD) queries. Extensive experiments demonstrate that our method achieves state-of-the-art performance on datasets with OOD queries, and can also generalize well to other identity-retrieval tasks. Importantly, our method outperforms the state-of-the-art by a large margin of 51.26% when the OOD query rate is around 50% on OUMVLP

Person recognition based on deep gait: a survey.

Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future