Computer Vision is one of the latest areas of computer science that studies the part of a machine’s vision
The DMS Vision team is working on an interactive healthcare cleaning robot system for the Silver Generation.
Object detection to detect people
Face recognition to recognize a particular user
Emotion Recognition for monitoring user emotions
Action Recognition to recognize the user’s motion
Pose Estimation to perform the user’s exercise assistance role
Fine-Grained Recognition
Detailed Recognition in Elderly Daily Monitoring Using Fine-Grained Classification Techniques
The system leverages advanced image analysis techniques that extract fine details, such as texture, color, and shape, from visual inputs to distinguish between similar items. Additionally, a fine-tuned, lightweight neural network is utilized, ensuring the system operates efficiently in real-world environments, with quick processing and high accuracy. This approach enables real-time monitoring, providing caregivers with precise and timely information about the elderly’s food and medication intake. We propose a fine-grained recognition framework that focuses on distinguishing these subtle differences, such as the slight visual differences between foods and medications and the variability in how they are consumed. This framework is composed of multiple models that extract features such as food characteristics, medication characteristics, and contextual factors like the dining environment, which are then integrated into a classification model to achieve accurate recognition. This fine-grained classification typically focuses on detecting minor visual differences. These differences often involve subtle variations in color, texture, shape, and patterns, which can be difficult to discern even with the human eye. Fine-Grained Visual Classification is an advanced image recognition technology that identifies subtle differences among similar categories of items. While general classification tasks distinguish clearly different categories, such as cats and dogs, fine-grained visual classification distinguishes more specific categories, such as various species of birds, different brands of cars, or similar-looking medications and foods. Fine-grained recognition is an important factor in elderly care, particularly for accurately identifying food and medication during daily monitoring. Distinguishing whether an elderly person is taking medication, drinking water, eating rice, or having soup, and further identifying the specific type of medication being consumed, has a significant impact on managing the health of the elderly.
Face and Emotion Recognition
Multi-task Emotion Recognition Based on Context-aware and Attention Module
Emotion recognition is an important research topic in the field of computer vision, and is the primary problem in affective computing, as well as the focus and difficulty of research. It would have a better impact on our lives if we could use inexpensive machines to monitor and understand the emotional information of others. However, there is currently no system that can do such a job. Because human emotional states are expressed in various ways, such as speech, expressions, actions, the environment in which a person is living, and various physiological signals, it is difficult to accurately reflect human emotions by relying on a single characteristic parameter and its features. We propose a framework, Muti-task Emotion Recognition (MTER), with four main models: face feature extraction model, body feature extraction model and context (scene) feature extraction model, and then fusion classification model. It is used to analyze images containing multiple people and recognize fused emotions based on face facial features, body features, and contextual information. The face feature and body feature extraction module takes the face and body parts of the image as input and the information implicit in the image such as facial expression, head position and body pose is extracted. In order to make the emotion recognition actively applied to real life, Fine-tuned Mobilenet lightweight network is utilized to reduce the computational effort and increase the recognition speed.
Pose and Activity Recognition
AiPE: Attention in Pose Estimation A transformer based pose estimation method
We propose a bottom-up human pose estimation method that combines the latest achievements in computer vision-transformer. Compared with the original version using VGG, our method uses a multi-headed self-attention mechanism to better localize the key points of the human body and connect the limbs. Even for some blurred, low-resolution images we can still perform human pose estimation, and our method achieves very good results in single and few people situations. However, in dense crowd conditions, our method does not perform human pose estimation as well as VGG, which stems from the weakness of transformer in analyzing small targets. It may also be due to the fact that we did not use the four block stack like the original swin transformer in order to reduce the computational effort and control the downsampling.
