WiMi Developed a 3D Human Behavior Recognition Algorithm System Based on Convolutional Neural Network

 WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that a 3D human behavior recognition algorithm system based on convolutional neural network (CNN), which has the good representational ability, was developed.

Human Behavior Recognition (HBR) is the process of deciphering human behaviors through sophisticated techniques in order to enable machines to understand, analyze, comprehend, and classify these behaviors and give any kind of valid input or stimulus. Deep learning is very effective in solving recognition and classification problems as it performs end-to-end optimization and related tasks can benefit each other (transfer learning).

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First, four unique spatio-temporal feature vectors are extracted from the relative motion of the skeletal joints, which are subsequently encoded into images that are then fed into a CNN for deep feature extraction. More specifically, the system is using data for a 3D human behavior recognition task by extracting four types of informative features (distance, distance velocity, angle, and angle velocity features) from the 3D skeleton data and encoding them into an image using a suitable encoding scheme. Additionally, WiMi uses inverse ion optimization to remove redundant and misleading information from the feature space. Finally, WiMi uses classification for the final prediction of operations.

The application process of the system mainly includes data collection, data pre-processing, feature extraction, classification, and prediction decision-making.

The first step is to collect appropriate data for the CNN-based 3D HBR system. An appropriate, structured and correctly labeled dataset is one of the most essential requirements for training the model. The neural network will use the dataset as an example to learn its corresponding feature information, save the memory of the deep neural network training, and use this memory as a basis for predicting other corresponding datasets. Therefore, the quality of the dataset will directly affect the quality of neural network training. A neural network trained on a dataset with wide coverage, full information and high resolution is far better than a network trained on a simple, low-resolution dataset with complex background. The requirements of human behavior recognition networks for the dataset include comprehensive behavioral categories, high-quality of behavior, clear video and so on. The second step is data pre-processing, where feature transformation, feature selection and feature extraction are coupled together, usually called data pre-processing modules. Feature extraction and correct representation are key steps to improve model performance. For high dimensional data, there is a relative increase in the likelihood of model overfitting, and therefore the selection of relevant features is required. Selecting the required features for the classification model is a critical step in getting it right. The next step is to classify, where the extracted features are used to train the model for the task of recognizing and classifying different forms of human behavior. Finally, there is predictive analytics, where more informative features are extracted from the inputs of the convolutional neural network model so that the model can make decisions without considering the visual differences between categories.

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WiMi’s 3D HBR algorithm system based on CNN technology can achieve high-precision behavioral identification of individuals and groups, set abnormal behavior prediction and timely warning, which can be widely used in personnel identification, vehicle identification, regional intrusion, target anomaly detection and other application scenarios.

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