Overview of ECO (Efficient Convolution Network for Online Video Understanding), its algorithms and implementation examples

Overview of ECO(Efficient Convolution Network for Online Video Understanding)

Efficient Convolutional Network for Online Video Understanding (ECO) is an efficient convolutional neural network (CNN)-based model designed for online video understanding, which reduces the traditional 3D CNN model’s It will reduce computational costs while maintaining high performance.

The main features of ECO are as follows

• Efficient network design: Combining 2D CNN (ResNet) and 3D CNN effectively captures temporal information while reducing computational cost. Optimizes the extraction of spatio-temporal information by using 2D convolution in the early stages and applying 3D convolution in the later stages.
• Lightweight and computationally efficient: Reduces the number of parameters and computational cost compared to existing C3D and I3D (Inflated 3D ConvNet). Particularly suitable for long videos and real-time processing.
• Two-stage structure (ECO & ECO-Enlite): ECO (full model): high accuracy, but slightly more computationally expensive; ECO-Enlite: more lightweight,
• Designed for real-time processing: ECO-Enlite (full model): more accurate, but slightly more computationally expensive
• Effective modeling of temporal information: designed to consider short video clips as well as longer time contexts.

Lower computational cost than traditional 3D CNNs while achieving high accuracy in action recognition (e.g., ActivityNet, Kinetics, etc.).
Well-balanced model suitable for real-time video analysis.

ECO is designed to maintain high performance while reducing computational cost, making it one of the most useful models in the field of online video understanding, which requires real-time processing.

Implementation Example

A common approach to implementing ECO (Efficient Convolutional Network for Online Video Understanding) is to use PyTorch. Below is an example of using a pre-trained model and a simple video classification implementation. 1.

1. install the necessary libraries

pip install torch torchvision numpy opencv-python

2. Load the ECO model: No official implementation of ECO is publicly available, but you can refer to the GitHub repository. Here is a simple example of an ECO implementation.

import torch
import torch.nn as nn
import torchvision.transforms as transforms
import cv2
import numpy as np

# Define a model (simplified version) of ECO
class SimpleECO(nn.Module):
    def __init__(self, num_classes=400):
        super(SimpleECO, self).__init__()
        self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), stride=1, padding=1)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
        self.fc = nn.Linear(64 * 7 * 7, num_classes)

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = torch.flatten(x, start_dim=1)
        x = self.fc(x)
        return x

# Create Model
num_classes = 101  # For the UCF101 dataset
model = SimpleECO(num_classes=num_classes)
model.eval()  # inference mode

3. Video preprocessing: Since ECO processes a short sequence of frames (clips) as input, it is necessary to split the video into frames and normalize them.

def load_video(video_path, num_frames=16, size=(224, 224)):
    cap = cv2.VideoCapture(video_path)
    frames = []
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    frame_interval = max(1, total_frames // num_frames)

    for i in range(num_frames):
        cap.set(cv2.CAP_PROP_POS_FRAMES, i * frame_interval)
        ret, frame = cap.read()
        if not ret:
            break
        frame = cv2.resize(frame, size)
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        frames.append(frame)

    cap.release()
    
    if len(frames) < num_frames:
        return None  # insufficient frame

    frames = np.array(frames).astype(np.float32) / 255.0  # normalization
    frames = np.transpose(frames, (3, 0, 1, 2))  # (C, T, H, W)
    return torch.tensor(frames).unsqueeze(0)  # (1, C, T, H, W)

video_tensor = load_video("sample_video.mp4")

4. inference using the model

if video_tensor is not None:
    with torch.no_grad():
        output = model(video_tensor)
        predicted_class = torch.argmax(output, dim=1).item()
    print(f"Predicted Class: {predicted_class}")
else:
    print("Insufficient frames in video")

5. application and development

• When using pre-trained models, refer to torchvision’s implementation of ECO incorporating ResNet.
• Fine tuning is possible with datasets such as UCF-101 and Kinetics-400.
• Take advantage of lightweighting (ECO-Lite) to achieve real-time processing.

This implementation is a simplified version, but it helps to get an idea of how ECO works. For a full-scale implementation, please refer to the ECO implementation on GitHub.

Application Examples

ECO (Efficient Convolutional Network for Online Video Understanding) has high computational efficiency in video understanding and is suitable for real-time processing.

1. Human Action Recognition

• Surveillance camera anomaly detection: Real-time detection of anomalous behavior (violent acts, suspicious movements) in public and commercial facilities; can be operated on edge devices (AI chips built into cameras), taking advantage of ECO’s lightweight nature.
• Sports Analytics: Analyzes player movement and identifies patterns of play in basketball and soccer game footage. Examples: automatic detection of goal scoring, dribbling vs. passing, etc.
• Medical Rehabilitation: Real-time analysis of rehabilitated patients’ movements to assess whether they are performing properly. Example: Prediction of fall risk by gait analysis.

2. automatic driving/ADAS (Advanced Driver Assistance Systems)

• Prediction of pedestrian behavior: Predicts pedestrian movements at intersections and supports braking control to avoid collisions. Example: Predicts the possibility of a pedestrian suddenly running out into the road.
• Driver Condition Monitoring: The system detects drowsiness and distraction in real time and alerts the driver. Example: ECO analyzes the driver’s blinking and head movements.
• Vehicle motion analysis: Recognizes and predicts in real time the acceleration, deceleration, lane change, and other behaviors of other vehicles.

3. Video Retrieval

• YouTube and TikTok content search: Analyzes scenes in videos and automates categorization into “sports,” “dance,” “cooking,” etc. Example: a user searches for “basketball dunk scene” and ECO extracts similar scenes.
• Broadcast archive management: Automatic tagging of specific actions (handshaking, walking, jumping, etc.) from news footage and movies.
• Crime Investigation: Quickly searches for behaviors such as “running” or “dropping something” in surveillance video to identify clues to a crime.

4. virtual reality (VR) / metaverse

• Gesture recognition for avatars: In the metaverse space, ECO analyzes the user’s real actions and reflects them in the avatar. Example: player’s punching actions are converted into in-game attacks.
• Interactive Fitness: Recognize the user’s exercise form in a VR fitness game and provide feedback on correct movement.

5. video captioning

• Automatic video captioning: Combines ECO and Natural Language Processing (NLP) to convert video content into text. Examples: “A man is playing tennis,” “A dog is running,” etc. are automatically generated.
• Barrier-free video service: Converts video content into narration in real time for people with disabilities.

6. robotics

• Abnormality detection in factories: Real-time detection of abnormal worker behavior (falls, accidents) using factory surveillance cameras.
• Visual recognition by household robots: A household robot recognizes human gestures with ECO and responds appropriately. Examples: “Wave your hand → return greeting”, “Pointing → move in the indicated direction”.

reference book

ECO: Efficient Convolutional Network for Online Video Understanding” is a paper proposing an efficient method for video understanding. Details of this paper are available on the arXiv.

arxiv.org

References for this paper include the following related studies

G. A. Sigurdsson et al. “What actions are needed for understanding human actions in videos?” ICCV’17.
H. Wang et al. “Dense trajectories and motion boundary descriptors for action recognition.” IJCV’13.
H. Wang and C. Schmid. “Action recognition with improved trajectories.” ICCV’13.
Joao Carreira and Andrew Zisserman. “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset.” arXiv:1705.07750 [cs.CV], 2017.
Bolei Zhou et al. “Temporal Relational Reasoning in Videos,” ECCV, 2018.

These references are important studies on video understanding and action recognition and have influenced the development of ECO.