Here's a basic outline of how you could structure your Python code for this project:
pythonimport numpy as np
import cv2
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, LSTM
# Step 1: Define functions for object detection and human activity recognition
def detect_objects(image):
# Implement object detection using YOLOv8 or another suitable model
# Return the coordinates and labels of detected objects
pass
def recognize_activity(frames_sequence):
# Implement human activity recognition using CNN and LSTM
# Return the recognized activity label
pass
# Step 2: Load pre-trained models or train new ones if necessary
# Load pre-trained YOLOv8 model for object detection
yolo_model = cv2.dnn.readNetFromDarknet('yolov8.cfg', 'yolov8.weights')
# Define and compile CNN model for human activity recognition
cnn_model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D((2, 2)),
Flatten(),
Dense(128, activation='relu'),
Dense(6, activation='softmax')
])
cnn_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Define and compile LSTM model for sequence processing
lstm_model = Sequential([
LSTM(64, input_shape=(None, 128)),
Dense(6, activation='softmax')
])
lstm_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# Step 3: Define main function for integrating object detection and activity recognition
def main():
# Capture video stream from camera or load video file
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
# Object detection
objects = detect_objects(frame)
# Extract regions of interest for each detected object
for obj in objects:
roi = frame[obj.y:obj.y+obj.h, obj.x:obj.x+obj.w]
# Preprocess ROI for human activity recognition
roi_resized = cv2.resize(roi, (64, 64))
roi_normalized = roi_resized / 255.0
# Predict human activity using CNN and LSTM
prediction = recognize_activity(roi_normalized)
# Display prediction on frame
cv2.putText(frame, prediction, (obj.x, obj.y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.imshow('Frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
main()
This code provides a basic structure for integrating object detection and human activity recognition. You'll need to implement the detect_objects() and recognize_activity() functions with appropriate algorithms and models. Additionally, make sure to adjust the model architecture and hyperparameters according to your specific requirements and dataset. Here's a breakdown of the project and a basic Python code structure to get you started. This is just a foundational framework, and you'll need to fill in the details based on your chosen approach:
Project Breakdown:
-
Research:
- Familiarize yourself with YOLOv8 for object detection and explore CNN/LSTM architectures for human activity recognition (HAR).
-
Integration Design:
- Choose an integration strategy. Here are two options:
- Two-stage approach: Use YOLOv8 to detect objects first, then use a separate HAR model on the detected object's bounding box region for activity recognition.
- Joint model approach: Explore research on combining object detection and HAR into a single model architecture.
- Choose an integration strategy. Here are two options:
-
Data Acquisition and Preprocessing:
- Find or create a suitable dataset containing images/videos with labeled objects and corresponding human activities.
- Preprocess the data (resizing, normalization) for training.
-
Model Training:
- Implement the chosen integration approach in Python using libraries like TensorFlow or PyTorch.
- Train the model on the prepared dataset, potentially fine-tuning pre-trained models like YOLOv8 for object detection and CNN/LSTM for HAR.
-
Evaluation:
- Define metrics like average precision (AP) for object detection and accuracy for HAR.
- Evaluate the model's performance on a hold-out test set or real-world scenarios.
Basic Python Code Structure (Example - Two-Stage Approach):
Remember:
- This is a basic structure, and you'll need to fill in the details based on your chosen approach and libraries.
- Explore research papers on integrating object detection and HAR for inspiration.
- Finding or creating a suitable dataset is crucial for training and evaluation.
- Consider incorporating data augmentation techniques to improve model robustness.
- Evaluate your model's performance on real-world scenarios and refine it as needed.
This project requires a strong understanding of deep learning, computer vision, and the chosen libraries. Good luck!
================================
Project: Integrating Object Detection and Human Activity Recognition
This Python code provides a basic framework to get you started on your final year project. It incorporates YOLOv8 for object detection and a simple CNN-LSTM architecture for human activity recognition. Remember, this is a starting point, and you'll need to fill in the details based on your specific chosen datasets and desired functionalities.
Libraries:
Python
import torch
from pathlib import Path
from torch.utils.data import DataLoader
from torchvision import transforms
# Object detection (replace with your preferred YOLOv8 implementation)
from models.yolo import Detector
# Human Activity Recognition (replace with your model implementation)
from models.har import ActivityRecognizer
Data Preparation (Replace with your specific datasets):
Python
# Define paths to your object detection and activity recognition datasets
object_detection_data_path = Path("path/to/object_detection/data")
activity_recognition_data_path = Path("path/to/activity_recognition/data")
# Define transformations for your datasets (e.g., resizing, normalization)
transform = transforms.Compose([
# ... your transformations
])
# Create DataLoaders for training and validation sets
object_detection_train_loader = DataLoader(...)
object_detection_val_loader = DataLoader(...)
activity_recognition_train_loader = DataLoader(...)
activity_recognition_val_loader = DataLoader(...)
Model Training (Replace with your chosen architectures):
Python
# Define object detection model (replace with YOLOv8 model definition)
object_detector = Detector(...)
# Define activity recognition model (replace with CNN-LSTM architecture)
activity_recognizer = ActivityRecognizer(...)
# Define optimizers and loss functions for both models
object_detector_optimizer = ...
object_detector_loss_fn = ...
activity_recognizer_optimizer = ...
activity_recognizer_loss_fn = ...
# Training loop (adjust hyperparameters and training epochs as needed)
for epoch in range(10):
# Train object detection model
object_detector.train()
for data, target in object_detection_train_loader:
# ... training steps for object detection model
# Train activity recognition model
activity_recognizer.train()
for data, label in activity_recognition_train_loader:
# ... training steps for activity recognition model
# Evaluate models on validation sets (replace with your evaluation metrics)
object_detector_eval_loss = ... # Calculate object detection validation loss
activity_recognizer_eval_accuracy = ... # Calculate activity recognition validation accuracy
print(f"Epoch: {epoch+1}, Object Detection Loss: {object_detector_eval_loss}, Activity Recognition Accuracy: {activity_recognizer_eval_accuracy}")
Integration and Real-world Application (Conceptual):
Python
# Define function to process a video frame
def process_frame(frame):
# Use object detector to detect objects in the frame
objects = object_detector(frame)
# Extract relevant information from detected objects (e.g., bounding boxes, class labels)
object_data = ...
# Crop frame based on object bounding boxes
cropped_frames = ...
# Use activity recognizer to classify activities in each cropped frame
activities = activity_recognizer(cropped_frames)
# Combine object information and recognized activities for analysis
# This could involve tracking object-activity relationships or triggering actions based on specific combinations
# ... your real-world application logic
# Continuously process video frames (replace with your video source)
while True:
frame = get_next_video_frame()
process_frame(frame)
Important Notes:
- This is a high-level structure. You'll need to implement the specific details of your chosen models, data preparation, training routines, and evaluation metrics.
- Consider techniques like transfer learning for pre-training your models on existing datasets.
- Explore more advanced architectures for human activity recognition, such as 3D CNNs or attention mechanisms.
- For real-world application, you'll need to define functionalities based on your chosen problem domain.
Further Resources:
- YOLOv8: https://github.com/topics/yolov8
- Human Activity Recognition with CNN-LSTM: https://m.youtube.com/watch?v=QmtSkq3DYko
Remember, this code provides a starting point, and you'll need to adapt it significantly based on your specific project goals and chosen datasets. Good luck with your final year project!
===================================================
Here's a breakdown of the project description and some initial Python code to get you started:
Project Overview:
This final year project aims to integrate object detection with human activity recognition for real-world applications. It involves:
- Researching cutting-edge object detection (YOLOv8) and human activity recognition (CNN-LSTM) algorithms.
- Designing a novel approach to combine these techniques.
- Training and fine-tuning a model using a large dataset.
- Evaluating the model's performance in real-world scenarios.
Python Code (Conceptual - Object Detection and Feature Extraction):
Python
# Import libraries
import cv2
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
# Load pre-trained YOLOv8 object detection model (replace with your choice)
model = cv2.dnn_DetectionModel("yolov8n.pt")
# Define function to detect objects and extract features
def detect_objects(frame):
# Preprocess the image (resize, normalization - replace with your logic)
preprocessed_image = preprocess_image(frame)
# Perform object detection with YOLOv8
classes, confidences, boxes = model.detect(preprocessed_image)
# Extract features from detected objects (replace with your specific features)
features = []
for (class_id, confidence, box) in zip(classes.flatten(), confidences.flatten(), boxes):
x_min, y_min, x_max, y_max = box
object_roi = frame[y_min:y_max, x_min:x_max] # Region of interest (ROI)
# Implement your logic to extract features from the ROI (e.g., size, color moments)
feature = extract_features(object_roi)
features.append((class_id, confidence, feature))
return features
# Example usage (replace with your actual video processing loop)
cap = cv2.VideoCapture(0) # Change to 1 for using camera
while True:
ret, frame = cap.read()
detected_objects = detect_objects(frame)
# Visualize detections (optional)
v = Visualizer(frame[:, :, ::-1]) # BGR to RGB for visualization
for (class_id, confidence, feature) in detected_objects:
v.draw_instance_predictions(class_id, confidence, frame)
cv2.imshow("Object Detection", v.get_image())
# Implement your human activity recognition logic using features
# (This part would involve building a separate CNN-LSTM model)
# Exit on 'q' key press
if cv2.waitKey(1) == ord("q"):
break
cap.release()
cv2.destroyAllWindows()
Things to Consider:
- Dataset Selection: Choose a dataset relevant to your chosen real-world application (e.g., fall detection, activity monitoring in a specific environment).
- Feature Engineering: Define the features you want to extract from the detected objects for human activity recognition.
- Human Activity Recognition Model: You'll need to build or adapt a CNN-LSTM model to classify human activities based on the extracted features.
- Evaluation Metrics: Define metrics (accuracy, precision, recall) to evaluate your model's performance in real-world scenarios.
Additional Resources:
- YOLOv8: https://github.com/topics/yolov8
- Detectron2 library: https://github.com/facebookresearch/detectron2
- Human Activity Recognition with CNN-LSTM: https://m.youtube.com/watch?v=QmtSkq3DYko
Remember: This is a basic example to get you started. You'll need to fill in the details and adapt the code to your specific project goals.
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