Human activity recognition or HAR is mainly a studied computer vision issue. The various applications of HAR will include health care, video surveillance, and human-computer interaction. As this form of imaging technique gets to advance, and the camera device keeps on upgrading, the novel approaches for the HAR will emerge constantly.
This review will aim to offer a comprehensive introduction to all kinds of video-based activity recognition, which will offer an overview of multiple approaches and evolutions by covering the modern approaches and classical literature.
More about human activities:
Human activities are known to have an inherent hierarchical structure. It indicates multiple levels of it and is considered to be a 3 level categorization.
- Firstly, you have the bottom level, where there is an atomic element, and the action primitives constitute some more complex human activities.
- Then you have the action primitive level, where the activity or action comes as the second level.
- Finally, you have the complex interactions to form the topmost level. It will refer to human activities involving more than two objects and persons.
Most of the research materials will follow the three-level categorizations, which will vary a bit but will maintain a consistent theme.
Action recognition in the video:
Humans get the chance to easily recognize and then identify actions in videos. But automating this same process can be a bit challenging. The Activity Recognition For Computer Vision is of the interest of applications like elderly behavior monitoring, automated surveillance, human-computer interaction, video summarization, and content-centric video retrieval.
- For example, while monitoring the activities of the daily elderly living, the recognition of the atomic actions such as falling, bending, and walking can be essential for the activity analysis.
- So far, the primary focus has always been towards the improvement of various components of the standard discriminative bottom-up framework. It forms a significant part of video recognition.
- In terms of the local salient motion feature detection, there are three major contributions. Then you have action classification and action representation as well.
The sensor-based and single-user activity recognition:
The sensor-based activity recognition will integrate emerging sensor networking areas with machine learning and novel data mining technologies. The main goal is to model a wider range of human activities.
- Mobile devices will offer enough sensor data and calculative power for enabling physical activity recognition. The main goal is to offer an estimation of energy consumed daily.
- Sensor-based activity recognition professionals state that by empowering the ubiquitous computers and the sensors for monitoring guests’ behavior, the computers are able to suit up towards the action on their behalf.
- The visual sensors, which will be adding the depth information and color like the Kinect, will allow some more accurate form of automatic action recognition. It will further fuse multiple emerging applications like smart environments and interactive education.
- The multiple views of the visual sensors will enable the development of machine learning, designed for automatic view-based invariant action recognition.
- Most of the advanced sensors as used in 3D motion capture systems will provide accurate automatic recognition even when there is a complicated version of the hardware system set up over here.
Levels under the sensor activity recognition:
The sensor-based activity recognition is one challenging task because of the inherently noisy nature of the given input. So, statistical modeling has been the main thrust in layers, where the recognition is conducted and connected at various intermediate levels. Right at the lowest level, where you can collect the sensor data, statistical learning will deal with ways to find detailed locations of the given agents from received signal data.
When it comes to the intermediate level, the statistical inference will be on ways to recognize the activities of individuals from inferred location sequences and the environmental conditions at the lowest levels. Then the highest concerning level is to find out the main goal or the sub-goals of agents from activity sequences through a mixture of statistical and logical reasoning.
The multi-user activity recognition:
Recognizing the activities for the multiple users using the on-body sensors first appeared in ORL with the help of active badge systems. During the early 1990s, this situation took place.
- Other sensor technologies like acceleration sensors were mostly used for identifying group activity-based patterns during the office scenes.
- Activities of the multiple users within the intelligent environments can be seen in Gu et al.
- Here, they get to investigate the major issues of recognizing activities for various users from sensor readings in a home environment. So, they proposed a novel pattern mining section for recognizing both multi-user and single-user activities within a unified solution.
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