Human Action Recognition Using CNN (Convolutional Neural Network) from Data Science

Human Action Recognition

As computing technology makes strides, many technology-based applications such as human-robot interaction, health care systems, 3D human body models, and dynamic motions have become popular. 

Human performance is linked to human body shape and related motions. 

Human activity recognition research is based on how the intricate movements of the human body are observed and analyzed. The recognition of actions using vision-based video is a task in which you can identify actions by watching the entire set of activities performed by humans. 

Over the last few decades, many techniques have been improved to create a more effective and efficient framework for action recognition. 

In this guide, we’ll review recent advancements regarding human action recognition. It will include the machine learning method, deep learning, and subsequent evaluation of these methods.

Why CNN?

Human behavior recognition is regarded as a representative pattern recognition problem, about which you can gain a deep understanding of it by doing a pg in data science.

The conventional method of researching behavior recognition uses decision trees, Support Vector Machines (SVM), and other machine-learning algorithms that can produce satisfactory results based on controlled environments.

The accuracy of these techniques is highly dependent on the efficacy and depth of manual feature extraction.

Furthermore, these methods can extract only not too in-depth features. 

Hence, the techniques for behavior recognition built on traditional pattern recognition have limitations in their classification accuracy.

CNN is a hierarchical system that works to build networks, much like funnels. It provides an entirely connected layer in which all networks are linked and output is processed.

The main benefit of CNN over other neural networks is that it detects the essential characteristics without human guidance. There is minimal dependence on preprocessing, and it is simple to comprehend and easy to implement. It is the most accurate of all algorithms to predict images.

A few words about HAR

Analyzing human movement from video footage is the most challenging task in many applications like computer vision & computer graphics. One of these applications, especially in computer animation, is the shifting of motion of a performer into a character to be animated. 

Since human motion is three-dimensional while video recordings contain just two dimensions, you must use some 3D-to-2D pose recovery and pose estimation techniques. 

Retargeting techniques are helpful only when you examine the pose reconstruction strategies. The study of pose recovery strategies will lead to understanding human behavior. This study is known as Human Action Recognition (HAR). The objective of HAR is to develop an intelligent machine that can accurately understand human behavior. Also, it should be great at understanding actions based on the footage.

What Else?

The nerve center of any intelligent system is its algorithm that can interpret human actions. Like the humans’ vision systems, it creates labels after studying the entire process of human movement. The method of creating such algorithms is seen in computer vision research. It further examines how machines can gain from digital videos and images, which improves their overall comprehension.

In computer vision, human movement ranges from bone and joint activities to complex movements that involve several joints/bones of the human body. Human action is dynamic and is recorded in a video, usually lasting only a few seconds. 

Human actions are done to complete a particular task, and some of them are achieved by simple actions. However, other actions may need a couple or several steps to complete. Action recognition is a critical task that can recognize complete human actions executed in a film under the current conditions. A video’s action is presented as a pixel array of pixels in every frame. 

However, computers don’t know how to construct the information describing the actions and deduce humans’ actions from this representation. Therefore, we can divide actions recognition problems into a representation of the action and action classification.

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How to use Deep Learning Model in Human Activity Recognition

When it comes to HAR, deep learning can be highly useful. Human activity recognition with smartphone accelerometers is one of the most exciting research that is currently possible.

HAR is among the classification issues for time series. This study used various machine learning and deep learning models to get highly effective results. 

You can describe the Long short-term memory (LSTM) model as a recurrent neural network capable of learning dependence on orders when solving sequence prediction problems.

Our test’s conclusion

We (me and my fellow students) took the knowledge that we got from our online data science course, and we decided to put it to use. We undertook experiments with various techniques and methods within our research project. 

The most important lesson we arrived at was the following:

In considering applications in future research, it is crucial to include sufficient but not excessive background, whether it’s unsupervised or supervised learning. 

The most important consideration is to determine the area from the image. We discovered that cropping is a powerful tool to achieve this effect. However, it is not advisable to crop too much or too small of a background. 

We also found that KNN works well when combined with the fine-tuning of CaffeNet models on our data. KNN is speedy in its calculation model. We will try and evaluate KNN with the complete 40-action dataset in the future. 

It is because CNN is very effective in identifying features. However, using it with KNN will yield an improved result instead of using low-level features.


Overall, CNN is a powerful tool for separating aspects from images; however, it can’t differentiate between object, subject, and background. Still, it surpasses the BoW model, as we expected from the research. 

Using SVM, KNN on CNN can provide even better quality in small-sized datasets than CNN alone. Using KNN and SVM in conjunction with the CNN feature is better if the dataset is small. I won’t say human action recognition can be done with perfection using CNN, but we can expect advancement to get the most accurate results. 

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