Overview of 3DCNN and examples of algorithms and implementations

Overview of 3DCNN

3DCNN (3D Convolutional Neural Network: 3D Convolutional Neural Network) is a type of deep learning model for processing mainly spatio-temporal data and data with three-dimensional features, and is an extension of 2DCNN (2D Convolutional Neural Network), which deals with image data, and is an extension of the 3DCNN (2D Convolutional Neural Network), which is a distinctive method in that it performs feature extraction in a three-dimensional space.

The main features of 3DCNN are as follows.

• 3D convolutional kernel: In 3DCNN, convolutional operations are performed in three dimensions. The kernel (filter) has height, width and depth (e.g. ( d times h times w )) and extracts local features from the entire input data. This makes it possible to capture spatio-temporal features in video data, as the time (frame) axis is also included.
• Input data: a common input data format is a four-dimensional tensor (e.g. ( C times D times H times W )).

– ( C ): number of channels (e.g. 3 for RGB images)
– ( D ): depth (e.g. number of frames, depth of volume data)
– ( H ): height
– ( W ): width

• Field of application.
  – Video analysis (action recognition, video classification, inter-frame motion detection)
  – Medical image analysis (3D volume data processing, e.g. MRI and CT scans)
  – 3D object recognition (analysis of point cloud data and voxel data)
  – Time series data processing (prediction and classification of data with spatio-temporal features)

Like regular CNNs, 3DCNNs consist of the following layers

• 3D convolutional layer: extracts local features using a kernel.
• 3D pooling layer: pooling operations (maximum and mean pooling) are performed in three dimensions to reduce the dimensionality of the features.
• Activation functions (e.g. ReLU): introduce non-linearity.
• Total coupling layer: finally, higher dimensional features are used for classification and prediction.

Advantages and disadvantages of using 3DCNN include

• Advantages.
  • Integrated learning of spatio-temporal information: 3DCNNs are suitable for video analysis and processing 3D data, as they can learn features that include a time axis.
  • Highly accurate feature extraction: local features are extracted in three dimensions, thus accurately capturing the spatial and temporal structure of the data.
• Disadvantages
  • High computational cost: the number of parameters and computational complexity increases because the kernel is 3D.
  • Large amount of data required: training the model requires a large amount of data and computational resources.

implementation example

Below is an example of implementing a 3DCNN using Python and TensorFlow/Keras. The example here is a classification problem using video data.

Assumptions.

• The video data must be decomposed into each frame and converted to 3D tensor format.
  • Data format: ((batch_size,depth,height,width,channel))
    • depth: number of frames (length of time axis)
    • height: Height of the frame
    • width: Width of the frame
    • channels: colour channels (3 for RGB)

Code example: 3DCNN implementation

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv3D, MaxPooling3D, Flatten, Dense, Dropout

# Shape of input data (depth, height, width, channels)
input_shape = (16, 64, 64, 3)  # Example: 64x64 RGB video with 16 frames

# Building a 3DCNN model.
model = Sequential([
    # 3D convolution layer 1
    Conv3D(filters=32, kernel_size=(3, 3, 3), activation='relu', input_shape=input_shape),
    MaxPooling3D(pool_size=(2, 2, 2)),
    
    # 3D convolution layer 2
    Conv3D(filters=64, kernel_size=(3, 3, 3), activation='relu'),
    MaxPooling3D(pool_size=(2, 2, 2)),

    # 3D convolution layer 3
    Conv3D(filters=128, kernel_size=(3, 3, 3), activation='relu'),
    MaxPooling3D(pool_size=(2, 2, 2)),

    # Flattened and connected to all coupling layers
    Flatten(),
    Dense(256, activation='relu'),
    Dropout(0.5),  # Use Dropout to prevent over-learning
    Dense(10, activation='softmax')  # Classification task with 10 classes.
])

# Compiling the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Model summary.
model.summary()

# Training data and labels (example) 
# X_train: video data (NumPy array, shape: [batch_size, depth, height, width, channels]) 
# y_train: labels (one-hot encoded) 
# Generate mock data and run demo
import numpy as np
X_train = np.random.rand(32, 16, 64, 64, 3)  # 32 random video data.
y_train = np.random.randint(0, 10, 32)      # 32 random class labels.
y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)

# Learning the model
model.fit(X_train, y_train, epochs=5, batch_size=8)

Code description

1. Model construction.
   • Spatio-temporal features of the video data are extracted in the 3D convolution layer (Conv3D).
   • The pooling layer (MaxPooling3D) reduces the dimensionality and reduces the computational load.
   • Finally, the data is flattened by Flatten and classified by the all-combining layer (Dense).
2. Data format.
   • Input data is a 5-dimensional tensor: (batch_size, depth, height, width, channels).
3. Training.
   • The model is trained with the fit method. Random data is generated for demonstration purposes.

Preparing data for training

When using real video data, the video needs to be broken down into frames, which need to be appropriately resized and converted into NumPy arrays. The following is an overview of the process.

import cv2
import numpy as np

def preprocess_video(video_path, target_frames=16, target_size=(64, 64)):
    cap = cv2.VideoCapture(video_path)
    frames = []
    
    while len(frames) < target_frames:
        ret, frame = cap.read()
        if not ret:
            break
        frame = cv2.resize(frame, target_size)
        frames.append(frame)
    
    cap.release()
    frames = np.array(frames, dtype='float32') / 255.0  # normalisation
    return frames[:target_frames]  # have the same number of frames

# Load video and convert to tensor.
video_tensor = preprocess_video('example_video.mp4')
print(video_tensor.shape)  # (16, 64, 64, 3)

References.

• When training with real data, pre-processing to align the number and size of frames is important.
• UCF101 and Kinetics are often used as data sets.

Application examples

Specific applications of 3DCNN (3D convolutional neural network) are described below.

1. video classification and action recognition

• Case study:
  • Analysis of sports videos: classification of sports events (e.g. football, basketball) and playing styles (e.g. goal-scenes, dribbling) from videos.
  • Security camera video analysis: abnormal behaviour recognition to detect suspicious behaviour (e.g. intrusion, placing objects).
• Practical applications:
  • Datasets: [UCF101](https://www.crcv.ucf.edu/data/UCF101.php), [Kinetics](https://deepmind.com/research/open-source/kinetics)
  • Example model (C3D model): C3D (Convolutional 3D) is a well-known 3DCNN model dedicated to sports action recognition.

2. medical field: analysis of 3D medical images

• Case studies:
  • Disease detection: analysing 3D volume data from MRI and CT scans to detect tumours and abnormal areas.
  • Organ segmentation: 3D images of organs such as lungs, brain and heart can be extracted to aid the diagnosis of diseases.
• Practical applications:
  • Early detection of Alzheimer’s disease: analysing MRI data using 3DCNN to detect changes in the brain to aid diagnosis.
  • Detection of pneumonia and new coronavirus infections: identifying signs of pneumonia from CT scans.
• Example dataset:
  [LUNA16](https://luna16.grand-challenge.org/) (lung nodule detection)
  [BraTS](https://www.med.upenn.edu/cbica/brats2020/) (brain tumour segmentation)

3. automated driving: 3D sensors and environment recognition

• Case studies:
  • LiDAR data analysis: recognition of surrounding objects and road conditions by analysing point cloud data from LiDAR (laser ranging sensors) installed in vehicles.
  • Vehicle behaviour prediction: Time-series prediction of the movements of surrounding vehicles and pedestrians.
• Practical applications:
  • 3D point cloud segmentation: LiDAR data is converted into voxel format and objects (e.g. cars, people, bicycles) are classified using 3DCNN.
  • Obstacle detection: Identification of the type and location of obstacles to assist in collision avoidance.
• Example dataset:
  [KITTI](http://www.cvlibs.net/datasets/kitti/) (a well-known dataset for automated driving research)

4. entertainment: 3D content generation and analysis

• Case studies:
  • Game motion analysis: recognising the movements of game characters from video and analysing player behaviour.
  • Automatic generation of 3D animations: using motion capture data to add realistic movements to 3D models.
• Practical applications:
  • Support for sports training: analysing players’ movements from videos and suggesting improvements in form.
  • Motion recognition in AR/VR systems: analysing user movements in AR/VR space in real time and reflecting them in the virtual environment.

5. astronomy and meteorology: 3D data analysis.

• Case studies:
  • Astronomical simulations: simulating the structure of galaxies and star clusters with 3D data to predict their evolution.
  • Meteorological data analysis: analysing 3D meteorological data (e.g. cloud formation, typhoon development) to improve prediction accuracy.
• Practical applications:
  • Weather simulation: predict cloud and rainfall distribution and issue warnings to prevent disasters.
  • Space telescope data analysis: analysing the formation processes of galaxies and stars from observational data.

6. sports and behaviour analysis: motion recognition

• Case study:
  • Human posture and behaviour analysis: analysing movement from video data for use in safety management and productivity improvement at work sites.
  • Fitness apps: check users’ exercise form in real time.
• Practical applications:
  • Safety management at industrial sites: monitor workers’ movements, detect inappropriate movements and prevent accidents.
  • Fitness trackers: applications that analyse yoga and muscle training movements and suggest improvements.

reference book (work)

This section describes reference books on 3D convolutional neural networks (3DCNNs) and related fields.

1. fundamentals of deep learning and CNNs in general
Books.
1. ‘Deep Learning’.
– Ian Goodfellow, Yoshua Bengio, Aaron Courville
– Translated editions are available. Provides detailed explanations of the basic theory of deep learning and how CNNs work, enabling users to acquire knowledge that will form the basis of applications to 3D data.
– [Detail page (Japanese version)](https://www.kspub.co.jp/book/detail/1528094.html)

2. ‘Pattern Recognition and Machine Learning’.
– Christopher M. Bishop
– Explains the theory of machine learning in general and helps to understand the statistical methods that are a prerequisite for learning CNNs.

Online resources
– Stanford CS231n: Convolutional Neural Networks for Visual Recognition
– A free course that teaches the basics of CNNs and touches on extensions to 3DCNNs.
– [Official CS231n page](http://cs231n.stanford.edu/)

2. video analysis and spatio-temporal data processing
1. ‘Computer Vision: Models, Learning, and Inference’
– Simon J.D. Prince.
– Fundamentals of image and video analysis. Provides extensive explanations on spatio-temporal feature extraction from video data. 2.

2. ‘Deep Learning for Video Game Programming’.
– Sebastian Koenig
– Ideas for applying 3DCNN to video and time-series data can be learned.

– Learning Spatiotemporal Features with 3D Convolutional Networks (C3D model paper)
– A fundamental paper on video analysis using 3DCNNs. Includes examples of applications in sports and action recognition.
– [paper link](https://arxiv.org/abs/1412.0767)

3. medical applications
1. ‘Deep Learning for Medical Image Analysis’.
– Editors: S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
– Describes practical examples of 3DCNN in medical image analysis.

2. ‘Medical Image Analysis and Deep Learning Algorithm’

3. Reviewing 3D convolutional neural network approaches for medical image segmentation

4. automated driving and LiDAR data analysis
Books.
1. ‘Deep Learning for Autonomous Vehicles Control’

2. ‘Robotics, Vision and Control’
– Peter Corke
– Fundamental computer vision techniques for 3D data processing and environmental awareness.

Online resources
– KITTI dataset and LiDAR analysis tutorial
– Helpful for learning 3D data analysis using benchmark datasets for automated driving.
– [KITTI official page](http://www.cvlibs.net/datasets/kitti/)

5. practice with Python and TensorFlow
Books.
1. ‘Deep Learning with Python, Second Edition’.

2. ‘Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow’.
– Aurélien Géron.
– Rich examples of implementations and easy to understand the steps of data analysis using 3DCNN.

Online resources.
– Official TensorFlow Guide (tutorials related to 3D CNNs).
– [TensorFlow Tutorials](https://www.tensorflow.org/tutorials)

6. advanced algorithms and theory
Books.
1. ‘Graph Neural Networks: Foundations, Frontiers, and Applications’
– Lingfei Wu
– Deals with graph neural networks (GNNs) in addition to 3DCNNs and is useful for analysing 3D structural data.

2. ‘Physics informed Neural Networks and Biologically inspired Machine Learning’