Fall detection holds immense importance in the field of healthcare, where timely detection allows for instant medical assistance. In this context, we propose a 3D ConvNet architecture which consists of 3D Inception modules for fall detection. The proposed architecture is a custom version of Inflated 3D (I3D) architecture, that takes compressed measurements of video sequence as spatio-temporal input, obtained from compressive sensing framework, rather than video sequence as input, as in the case of I3D convolutional neural network. This is adopted since privacy raises a huge concern for patients being monitored through these RGB cameras. The proposed framework for fall detection is flexible enough with respect to a wide variety of measurement matrices. Ten action classes randomly selected from Kinetics-400 with no fall examples, are employed to train our 3D ConvNet post compressive sensing with different types of sensing matrices on the original video clips. Our results show that 3D ConvNet performance remains unchanged with different sensing matrices. Also, the performance obtained with Kinetics pre-trained 3D ConvNet on compressively sensed fall videos from benchmark datasets is better than the state-of-the-art techniques.
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning. While these innovative and ground-breaking applications can be considered as a boon, at the same time they raise significant privacy concerns. In fact, recent GDPR (General Data Protection Regulation) legislation has highlighted and become an incentive for privacy-preserving solutions. Typical privacy-preserving video monitoring schemes address these concerns by either anonymizing the sensitive data. However, these approaches suffer …
Towards Privacy-Supporting Fall Detection via Deep Unsupervised RGB2Depth Adaptation
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人机交互与机器人研究所）；Xiong’an Institute of Innovation, Chinese Academy of Science（中国科学院雄安创新研究院）；Institute of Automation, Chinese Academy of Science（中国科学院自动化研究所）
Manifold amount of video data gets generated every minute as we read this document, ranging from surveillance to broadcasting purposes. There are two roadblocks that restrain us from using this data as such, first being the storage which restricts us from only storing the information based on the hardware constraints. Secondly, the computation required to process this data is highly expensive which makes it infeasible to work on them. Compressive sensing(CS)[2] is a signal process technique[11], through optimization, the sparsity of a signal can be exploited to recover it from far fewer sample…
Deep networks can be trained to map images into a low-dimensional latent space. In many cases, different images in a collection are articulated versions of one another; for example, same object with different lighting, background, or pose. Furthermore, in many cases, parts of images can be corrupted by noise or missing entries. In this paper, our goal is to recover images without access to the ground-truth (clean) images using the articulations as structural prior of the data. Such recovery problems fall under the domain of compressive sensing. We propose to learn autoencoder with tensor ring …
Privacy-Preserving Multi-Stage Fall Detection Framework with Semi-supervised Federated Learning and Robotic Vision Confirmation
Analytics Everywhere Lab, University of New Brunswick, Canada（分析无界实验室，新不伦瑞克大学，加拿大）；SE+AI Research Lab, University of New Brunswick, Canada（SE+AI研究实验室，新不伦瑞克大学，加拿大）；National Research Council, New Brunswick, Canada（国家研究理事会，新不伦瑞克，加拿大）；RMIT University, Australia（皇家墨尔本理工大学，澳大利亚）
P2MFDS: A Privacy-Preserving Multimodal Fall Detection System for Elderly People in Bathroom Environments
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Department of Computer Science and Software Engineering, The University of Western Australia（西澳大学计算机科学与软件工程系）
KITE Research Institute, Toronto Rehabilitation Institute – University Health Network（KITE研究所，多伦多康复研究所——大学健康网络）；Institute of Biomedical Engineering, University of Toronto（多伦多大学生物医学工程研究所）
Falls are one of the leading cause of injury-related deaths among the elderly worldwide. Effective detection of falls can reduce the risk of complications and injuries. Fall detection can be performed using wearable devices or ambient sensors; these methods may struggle with user compliance issues or false alarms. Video cameras provide a passive alternative; however, regular RGB cameras are impacted by changing lighting conditions and privacy concerns. From a machine learning perspective, developing an effective fall detection system is challenging because of the rarity and variability of fall…
Indian Institute of Technology Ropar（印度理工学院鲁尔基分校）；Monash University（莫纳什大学）；KITE -- University Health Network（KITE——大学健康网络）
Automatic fall detection is a vital technology for ensuring the health and safety of people. Home-based camera systems for fall detection often put people's privacy at risk. Thermal cameras can partially or fully obfuscate facial features, thus preserving the privacy of a person. Another challenge is the less occurrence of falls in comparison to the normal activities of daily living. As fall occurs rarely, it is non-trivial to learn algorithms due to class imbalance. To handle these problems, we formulate fall detection as an anomaly detection within an adversarial framework using thermal imag…
Shanghai Jiao Tong University（上海交通大学）；Stanford University（斯坦福大学）；Fudan University（复旦大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Michigan State University（密歇根州立大学）
When a measurement falls outside the quantization or measurable range, it becomes saturated and cannot be used in classical reconstruction methods. For example, in C-arm angiography systems, which provide projection radiography, fluoroscopy, digital subtraction angiography, and are widely used for medical diagnoses and interventions, the limited dynamic range of C-arm flat detectors leads to overexposure in some projections during an acquisition, such as imaging relatively thin body parts (e.g., the knee). Aiming at overexposure correction for computed tomography (CT) reconstruction, we in thi…
This paper proposes a real-time embedded fall detection system using a DVS(Dynamic Vision Sensor) that has never been used for traditional fall detection, a dataset for fall detection using that, and a DVS-TN(DVS-Temporal Network). The first contribution is building a DVS Falls Dataset, which made our network to recognize a much greater variety of falls than the existing datasets that existed before and solved privacy issues using the DVS. Secondly, we introduce the DVS-TN : optimized deep learning network to detect falls using DVS. Finally, we implemented a fall detection system which can run…
Privacy-Preserving Action Recognition: Taxonomy, Methods, and Privacy-Utility Trade-offs
Chung-Ang University（中央大学）；Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院）；College of Art and Technology, Chung-Ang University（中央大学艺术与技术学院）
University of North Carolina Charlotte（北卡罗来纳大学夏洛特分校）；Washington State University（华盛顿州立大学）；Azusa Pacific University（阿苏萨太平洋大学）
This paper takes initial strides at designing and evaluating a vision-based system for privacy ensured activity monitoring. The proposed technology utilizing Artificial Intelligence (AI)-empowered proactive systems offering continuous monitoring, behavioral analysis, and modeling of human activities. To this end, this paper presents Single Run Action Detector (S-RAD) which is a real-time privacy-preserving action detector that performs end-to-end action localization and classification. It is based on Faster-RCNN combined with temporal shift modeling and segment based sampling to capture the hu…
In recent years a growing interest on action recognition is observed, including detection of fall accident for the elderly. However, despite many efforts undertaken, the existing technology is not widely used by elderly, mainly because of its flaws like low precision, large number of false alarms, inadequate privacy preserving during data acquisition and processing. This research work meets these expectations. The work is empirical and it is situated in the field of computer vision systems. The main part of the work situates itself in the area of action and behavior recognition. Efficient algo…
KITE – Toronto Rehabilitation Institute, University Health Network（KITE – 多伦多康复研究所，大学健康网络）；University of Toronto（多伦多大学）
Fall detection is an important problem from both the health and machine learning perspective. A fall can lead to severe injuries, long term impairments or even death in some cases. In terms of machine learning, it presents a severely class imbalance problem with very few or no training data for falls owing to the fact that falls occur rarely. In this paper, we take an alternate philosophy to detect falls in the absence of their training data, by training the classifier on only the normal activities (that are available in abundance) and identifying a fall as an anomaly. To realize such a classi…
Neuromorphic vision-based sensors are gaining popularity in recent years with their ability to capture Spatio-temporal events with low power sensing. These sensors record events or spikes over traditional cameras which helps in preserving the privacy of the subject being recorded. These events are captured as per-pixel brightness changes and the output data stream is encoded with time, location, and pixel intensity change information. This paper proposes and benchmarks the performance of fine-tuned conventional vision models on neuromorphic human action recognition and fall detection datasets.…
