Overview of ST-CNN and examples of algorithms and implementations

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Overview of ST-CNN

ST-CNN (Spatio-Temporal Convolutional Neural Network) is a type of convolutional neural network (CNN) designed to process spatio-temporal data (e.g. video, sensor data, time-series images, etc.) and extends conventional CNNs to The aim of the method is to learn spatial (Spatio) and temporal (Temporal) features simultaneously.

Features of ST-CNNs include the following

  • Integrated processing of spatio-temporal data: ST-CNNs simultaneously learn spatial features (shapes and patterns) in images and videos, as well as movements and changes in time.
    – Examples: object motion in videos and temporal changes in medical data.
  • Use of 3D convolution: ST-CNNs typically employ 3D convolution. This enables spatial (height and width) and temporal (frames and time series) analysis in a unified manner; the operation of 3D convolution is to learn correlations between neighbouring frames using a 3D filter to extract spatial and temporal features.
  • Exploiting continuity between frames: temporal continuity is important in video and sensor data, and ST-CNNs improve prediction and classification accuracy by effectively learning dependencies between consecutive frames.

The basic architecture of ST-CNN is a combination of the following blocks

  • Input data.
    • For video: height x width x number of frames (time) x number of channels (e.g. RGB).
    • For time series data: feature dimension x time.
  • 3D convolution layer
    • Simultaneous extraction of spatial and temporal features using 3D filters. Example: ( K times K times T ) (spatial width (K), height (K), time (T)).
  • Pooling layer: dimensionality reduction of features. E.g. 3D pooling (maximum pooling or average pooling) to reduce space and time axis.
  • Total Combining Layer (or Global Pooling): task-specific prediction using the extracted features.
  • Output layer: task-specific outputs, e.g. classification, regression, segmentation.

Typical ST-CNN models include.

  • C3D (Convolutional 3D Network): a 3D CNN model dedicated to video analysis, representing a basic form of ST-CNN and capturing both spatial and temporal features.
  • I3D (Inflated 3D ConvNet): a 2D CNN model extended to a 3D CNN. It performs well for training on large video datasets (e.g. Kinetics).

The advantages and challenges of ST-CNNs are as follows

  • Advantages.
    • Capable of learning spatial and temporal features simultaneously, thus capturing the complex dependencies of the data.
    • Specialised and high performance for analysing video and time-series data.
  • Challenges
    • High computational cost: 3D convolution consumes more hardware resources than 2D convolution due to the higher number of parameters and computational complexity.
    • Data pre-processing and normalisation: due to the large size of spatio-temporal data, data normalisation and sampling may be required.
implementation example

The following is a simple example of a Python implementation of an ST-CNN (Spatio-Temporal Convolutional Neural Network). The code treats video data as input and uses 3D convolutional layers to extract spatio-temporal features. The widely used Keras is used as a library.

Example implementation of ST-CNN: video classification task

Install the necessary libraries: install the necessary libraries using the following command.

pip install tensorflow opencv-python numpy

code example

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv3D, MaxPooling3D, Flatten, Dense, Dropout
import numpy as np
import cv2
import os

# Shape settings for video data (e.g. number of frames 16, resolution 112x112, RGB channels)
INPUT_SHAPE = (16, 112, 112, 3)  # (time, height, width, channels)

# model definition
def build_st_cnn(input_shape, num_classes):
    model = Sequential([
        Conv3D(32, kernel_size=(3, 3, 3), activation='relu', input_shape=input_shape),
        MaxPooling3D(pool_size=(2, 2, 2)),

        Conv3D(64, kernel_size=(3, 3, 3), activation='relu'),
        MaxPooling3D(pool_size=(2, 2, 2)),

        Conv3D(128, kernel_size=(3, 3, 3), activation='relu'),
        MaxPooling3D(pool_size=(2, 2, 2)),

        Flatten(),
        Dense(128, activation='relu'),
        Dropout(0.5),
        Dense(num_classes, activation='softmax')
    ])
    return model

# Functions for pre-processing videos
def preprocess_video(video_path, frame_count=16, frame_size=(112, 112)):
    cap = cv2.VideoCapture(video_path)
    frames = []
    while len(frames) < frame_count and cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        frame = cv2.resize(frame, frame_size)  # resize
        frames.append(frame)
    cap.release()
    # Zero padding when frames are insufficient.
    while len(frames) < frame_count:
        frames.append(np.zeros_like(frames[0]))
    return np.array(frames)

# Sample video data creation
def create_dummy_data(num_samples=100, num_classes=5):
    x_data = np.random.rand(num_samples, 16, 112, 112, 3)  # Dummy video data
    y_data = np.random.randint(num_classes, size=(num_samples,))  # dummy label
    y_data = tf.keras.utils.to_categorical(y_data, num_classes=num_classes)  # one-hot encoding
    return x_data, y_data

# model building
NUM_CLASSES = 5
model = build_st_cnn(INPUT_SHAPE, NUM_CLASSES)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# dummy data generation
x_train, y_train = create_dummy_data()

# model learning
model.fit(x_train, y_train, batch_size=8, epochs=5)

# Show model structure
model.summary()

3. when using real datasets

  • Prepare video files: use datasets such as UCF101 or Kinetics.
  • Video pre-processing: the following is an example of pre-processing a video file into a format that can be input into the model.
# List paths to video files.
video_paths = ["path/to/video1.mp4", "path/to/video2.mp4"]

# Data preparation by applying pre-processing.
x_data = np.array([preprocess_video(path) for path in video_paths])
y_data = np.array([0, 1])  # Class labels (e.g.)
y_data = tf.keras.utils.to_categorical(y_data, num_classes=NUM_CLASSES)

# Learning by model
model.fit(x_data, y_data, batch_size=2, epochs=10)

Supplementary information.

  1. Conv3D: learns spatial (height x width) and temporal (between frames) information of the video data simultaneously.
  2. Dealing with insufficient data: increase training data using data expansion (flipping, cropping, adding noise, etc.).
    Transfer learning: using pre-trained models, e.g. Kinetics, enables efficient learning.

References.

Application examples

Specific areas and examples are described below.

1. video classification and action recognition

  • Case study (action classification in videos): detecting and classifying specific actions (e.g. goals in football, serves in tennis) in sports videos. Dataset: UCF101, Kinetics
  • Application areas (sports analysis, surveillance systems): systems for detecting abnormal behaviour (e.g. fights, theft) in surveillance video. Safety measures in shopping malls and public transport.
  • Specific examples
    Paper: ‘Spatiotemporal Convolutional Networks for Action Recognition’ (Simonyan & Zisserman, 2014).

2. medical field

3. automatic driving

  • Case study (understanding road conditions): analysing in-vehicle camera footage to predict the movement of pedestrians and traffic signals.
  • Applications: vehicle collision prevention, pedestrian movement prediction
    Detection of weather changes: adaptive control of the driving environment by recognising weather conditions such as snow and rain from video data.
  • Examples: automatic driving systems such as Tesla and Waymo apply similar methods to the analysis of in-vehicle camera footage.

4. entertainment

  • Case study (video recommendation systems): video distribution platforms (Netflix, YouTube, etc.) analyse the content and features of videos and make recommendations that are most suitable for individuals.
  • Case study (personalised video delivery): analysing actors’ facial expressions and emotions in video productions to improve the emotional expression of characters.

5. weather analysis

  • Case study (weather forecasting): predict the occurrence of disasters (typhoons, torrential rains, etc.) by analysing time-series data from satellite images and weather maps.
  • Case study (disaster forecasting, weather services for agriculture): detecting signs of eruptions from volcanic activity monitoring images and thermal images.

6. sports analysis

  • Case study (play performance analysis): analysing player movements and team strategies from video footage of football, baseball, basketball and other matches.
  • Case study (optimising team strategy and player evaluation): automatic highlight generation of goals and scoring scenes: automatically extracts important scenes from a match to efficiently generate highlight videos.

7. integration with natural language processing

  • Case study (video caption generation): automatically interprets the content from video data and generates text captions.
  • Case study (video content description app for the visually impaired): analyses meeting and interview footage, converts the spoken content into text and summarises it.

8. retail and marketing

reference book (work)

The following are references related to ST-CNNs (Spatio-Temporal Convolutional Neural Networks).

1. fundamentals of deep learning and spatio-temporal data analysis
Book title.
– ‘Deep Learning’.
Author(s): Ian Goodfellow, Yoshua Bengio, Aaron Courville
Year of publication: 2016
Publisher: MIT Press
Abstract: Provides a comprehensive overview of the theoretical foundations of deep learning. RNNs and CNNs that can be applied to time series data and video analytics are also covered.

– ‘Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow’.
Author(s): Aurélien Géron
Year of publication: 2019 (2nd edition)
Publisher: O’Reilly Media
Abstract: A practical book for learning deep learning models through implementation. It can be applied to video analysis and spatio-temporal modelling.

2. video analysis and ST-CNN related techniques
Book title.
– ‘Deep Learning for Computer Vision: expert techniques to train advanced neural networks for vision tasks’.
Author: rajalingappaa Shanmugamani
Publication year: 2018
Publisher: Packt Publishing
Abstract: Describes deep learning methods in computer vision, including applications of ST-CNN and 3D CNN to video analytics.

– ‘Deep Learning for Video-Based Human Action Recognition’.
Author(s): Liang Wang, Guoying Zhao, Li Cheng
Publication year: 2019
Publisher: springer
Abstract: A book on the subject of human action recognition, detailing practical examples of ST-CNN for spatio-temporal features.

– ‘Computer Vision: Algorithms and Applications’.
Author: Richard Szeliski
Year of publication: 2021 (2nd edition)
Publisher: Springer
Abstract: Covers a wide range of computer vision algorithms. Some spatio-temporal issues are also covered.

3. time series data and video processing
– ‘Deep Learning Models for Time Series Forecasting: A Review’

– ‘Learning Spatio-Temporal Features with 3D Convolutional Neural Networks’.
Published in paper format, but can be used as a complement to basic books. Particularly relevant for video analysis of C3D (3D CNN) models.

4. applications and specific examples
Book title.
– ‘Multimedia Data Mining and Analytics: Disruptive Innovation’
Author: Aaron K. Baughman
Year of publication: 2015
Publisher: Springer
Abstract: Covers techniques for analysing multimedia data, useful for ST-CNN applications.

– ‘Video Analytics Using Deep Learning’.
Author(s): Amit Kumar Singh, Pradeep Kumar Mallick
Year of publication: 2022
Publisher: Wiley
Abstract: Provides a comprehensive overview of the theory and practice of deep learning in video analytics. Particularly relevant chapters on ST-CNNs.

5. practical guide
Book title.
– ‘Practical Deep Learning for Cloud, Mobile, and Edge’
Authors: anirudh Koul, Siddha Ganju, Meher Kasam
Publication year: 2019
Publisher: O’Reilly Media
Abstract: Explains deep learning with a practical approach. It also touches on video analytics applications.

– ‘Python Machine Learning by Example’.
Author: Yuxi (Hayden) Liu
Year of publication: 2020
Publisher: Packt Publishing
Abstract: Provides concrete examples of machine learning using Python. Also useful for spatio-temporal data analysis applications.

6. datasets and hands-on
Online resources.
– UCF101, Kinetics dataset description: ideal dataset for action recognition.
– Official PyTorch and TensorFlow tutorials: examples of video analysis implementations using 3D CNN and ST-CNN.

– ‘Learning Spatiotemporal Features with 3D Convolutional Networks’.
– Basic research papers related to ST-CNNs.
– [paper link](https://arxiv.org/abs/1412.0767)

– ‘Action Recognition using Visual Attention and Temporal Convolutional Networks’
– An application of ST-CNNs specifically for video analysis.

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