Realtime Object Detection

Welcome to the era where machines have grown to see and understand the world around us. Real time Object Detection using Machine Learning is revolutionizing how we interact with technology, making our lives more efficient and secure. In this article, we will explore the technology’s applications, algorithms, and how real time object detection impact on various industries object. At the end of the article, we will learn how to make real time object detection in Python. Source code can also help you make a real time object detection project.

What is Real-Time Object Detection?

Real time object detection is a computer vision technique in which system helps to detect and locate the object in a video or image in real time. So, the existing system of real time object detection take more time and lack of speed to process input data and identifying the object. Traditional method require numerous passes over an image or video to detect object but this system used YOLO advanced algorithm. YOLO is more faster and efficient than typical object detection algorithms.

How Does It Work?

The magic behind real-time object detection lies in machine learning and computer vision. Convolutional Neural Networks (CNNs) algorithm play a important role in breaking down an image into smaller parts and analyzing them layer by layer to recognize patterns and features.

Applications of Realtime Object Detection

Enhancing Security with Surveillance Systems

In the realm of security, realtime object detection is a game-changer. From identifying intruders to detecting unusual behavior, this technology ensures swift response, reducing the risk of security breaches.

Revolutionizing Autonomous Vehicles

Autonomous vehicles heavily rely on real-time object detection to navigate through dynamic environments. From recognizing pedestrians to avoiding obstacles, this technology is the eyes and ears of self-driving cars.

Retail's Personal Shopping Assistant

In the retail sector, real-time object detection is transforming the shopping experience. Smart mirrors can suggest outfits based on a customer’s choice, utilizing this technology to identify clothing items and accessories in real-time.

Healthcare's Diagnostic Support

Within healthcare, real-time object detection aids in medical imaging, helping identify and diagnose conditions faster. From identifying tumors in X-rays to monitoring patient movements, the applications are vast.

Key Algorithms in Real-Time Object Detection

YOLO (You Only Look Once) Algorithm

The YOLO algorithm is a pioneer in realtime object detection, dividing an image into a grid and predicting bounding boxes and class probabilities for each cell simultaneously. This leads to speedy and accurate results.

Faster R-CNN (Region-based Convolutional Neural Network)

Faster R-CNN introduced Region Proposal Networks (RPN) to generate potential bounding box proposals, enhancing accuracy in object detection. This algorithm strikes a balance between speed and precision.

SSD (Single Shot Multibox Detector)

SSD is renowned for its efficiency, performing object detection in a single forward pass through a neural network. It utilizes multiple scales to detect objects of various sizes, making it robust and adaptable.

Realtime object Detection in Python

First you need to install required libraries and caffe model.Download Caffe Model

To make realtime object detection in python follow theses steps:

Step 1: Importing Libraries

				# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2

# import the necessary packages
from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2

This block imports necessary libraries for video processing and computer vision.
VideoStream and FPS are from the imutils library.
numpy is imported as np.

argparse is used for command-line argument parsing.
imutils, time, and cv2 are standard libraries for image processing.

Step 2: Parsing Command-Line Arguments

				# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
    help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
    help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
    help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", required=True,
    help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", required=True,
    help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
    help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

This block uses argparse to handle command-line arguments.

Three arguments are defined:
- -p or --prototxt: Path to Caffe ‘deploy’ prototxt file (required).
- -m or --model: Path to Caffe pre-trained model (required).
- -c or --confidence: Minimum probability to filter weak detections (default is 0.2).

Step 3: Defining Classes and Colors

				CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
    "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
    "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
    "sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
    "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
    "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
    "sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

This block defines a list of class labels (CLASSES).
Random RGB colors are generated for each class and stored in COLORS.

Step 4: Loading Caffe Model

				# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

The pre-trained Caffe model is loaded using cv2.dnn.readNetFromCaffe.
The paths to the prototxt file and the model file are obtained from the command-line arguments.

Step 5: Initializing Video Stream and FPS Counter

				# initialize the video stream, allow the camera sensor to warm up,
# and initialize the FPS counter
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)
fps = FPS().start()

# initialize the video stream, allow the camera sensor to warm up,
# and initialize the FPS counter
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)
fps = FPS().start()

Video stream is initialized using VideoStream with the default camera source (src=0).

There is a 2-second delay (time.sleep(2.0)) to allow the camera sensor to warm up.
FPS counter (fps) is initialized.

Step 6: Main Loop for Video Processing

				# loop over the frames from the video stream
while True:
    # grab the frame from the threaded video stream and resize it
    # to have a maximum width of 400 pixels
    frame = vs.read()
    frame = imutils.resize(frame, width=400)
    # grab the frame dimensions and convert it to a blob
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
        0.007843, (300, 300), 127.5)
    # pass the blob through the network and obtain the detections and predictions
    net.setInput(blob)
    detections = net.forward()

# loop over the frames from the video stream
while True:
    # grab the frame from the threaded video stream and resize it
    # to have a maximum width of 400 pixels
    frame = vs.read()
    frame = imutils.resize(frame, width=400)
    # grab the frame dimensions and convert it to a blob
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
        0.007843, (300, 300), 127.5)
    # pass the blob through the network and obtain the detections and predictions
    net.setInput(blob)
    detections = net.forward()

The main loop iterates over frames from the video stream.

Each frame is resized to have a maximum width of 400 pixels.
A blob is created from the resized frame for further processing.
The blob is passed through the neural network, and detections and predictions are obtained.

Step 7: Loop Over Detections and Drawing Bounding Boxes

				    # loop over the detections
    for i in np.arange(0, detections.shape[2]):
        # extract the confidence (i.e., probability) associated with the prediction
        confidence = detections[0, 0, i, 2]
        # filter out weak detections by ensuring the `confidence` is greater than the minimum confidence
        if confidence > args["confidence"]:
            # extract the index of the class label from the `detections`,
            # then compute the (x, y)-coordinates of the bounding box for the object
            idx = int(detections[0, 0, i, 1])
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")
            # draw the prediction on the frame
            label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
            cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2)
            y = startY - 15 if startY - 15 > 15 else startY + 15
            cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
    # show the output frame
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF
    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break
    # update the FPS counter
    fps.update()

    # loop over the detections
    for i in np.arange(0, detections.shape[2]):
        # extract the confidence (i.e., probability) associated with the prediction
        confidence = detections[0, 0, i, 2]
        # filter out weak detections by ensuring the `confidence` is greater than the minimum confidence
        if confidence > args["confidence"]:
            # extract the index of the class label from the `detections`,
            # then compute the (x, y)-coordinates of the bounding box for the object
            idx = int(detections[0, 0, i, 1])
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")
            # draw the prediction on the frame
            label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
            cv2.rectangle(frame, (startX, startY), (endX, endY), COLORS[idx], 2)
            y = startY - 15 if startY - 15 > 15 else startY + 15
            cv2.putText(frame, label, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
    # show the output frame
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF
    # if the `q` key was pressed, break from the loop
    if key == ord("q"):
        break
    # update the FPS counter
    fps.update()

This block loops over the detections obtained from the neural network.
Weak detections (based on confidence threshold) are filtered out.
Bounding boxes are drawn on the frame for the detected objects, along with class labels and confidence percentages.

The frame is displayed, and user input is checked to break out of the loop if the ‘q’ key is pressed.

Step 8: Updating and Displaying FPS Information

				# stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

# stop the timer and display FPS information
fps.stop()
print("[INFO] elapsed time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

The FPS counter is stopped, and elapsed time and approximate FPS are printed to the console.

Step 9: Cleanup

				# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()

OpenCV windows are closed.

Video stream is stopped, and resources are released.

Output

In conclusion, real-time object detection using machine learning is not just a technological wonder; it’s a transformative force across various industries. From maintaining security to enhancing our shopping experiences, the applications are limitless. As algorithms evolve, overcoming challenges like perplexity and burstiness, we can expect even more breakthroughs, making our world brighter and safer.

To learn more about realtime object detection

How fast is real-time object detection?

Real-time object detection is incredibly fast, often processing video feeds at several frames per second, allowing for instant analysis and response.

Can real-time object detection be used on mobile devices?

Yes, many real-time object detection models are optimized for mobile devices, making them suitable for applications like augmented reality and mobile photography.

Is real-time object detection only for images and videos?

While commonly used in images and videos, real-time object detection can also be applied to live camera feeds and streaming data, extending its applications to real-time monitoring.