human detection and activity recognition

Suppose, we have number of robots and humans. we need to count number of humans. then which methodology should be adopted to distinghish the human from the robots?

[prabhat kumar sharma]

Hello Diksha,

To distinguish humans from robots in MATLAB, you can use image processing and computer vision techniques. A simple approach involves using a pre-trained machine learning or deep learning model to classify objects in images or video frames. Here's a basic outline of the steps you can take:

Steps to Distinguish Humans from Robots

1. Capture Image/Video:

- Use a camera to capture images or video frames containing both humans and robots.

2. Pre-process the Image:

- Convert the image to grayscale or adjust its resolution if necessary to simplify processing.

3. Feature Extraction:

- Extract features from the image that can help distinguish between humans and robots. This might include shape, size, or other identifiable characteristics.

4. Use a Pre-trained Model:

- Utilize a pre-trained object detection model that can identify humans. Common models include YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), or models available in MATLAB's Deep Learning Toolbox.

- MATLAB provides access to models like `YOLOv2`, `YOLOv3`, and the `ResNet` family, which can be used for object detection tasks.

5. Post-process and Count

- Analyze the detection results to count the number of humans identified in the image or video frame.

Example Using a Pre-trained Model in MATLAB

% Load a pre-trained YOLO v2 network
net = yolov2ObjectDetector('tiny-yolov2-coco');

% Read the image
img = imread('your_image_path.jpg');

% Detect objects in the image
[bboxes, scores, labels] = detect(net, img);

% Filter detections for humans
humanIdx = strcmp(labels, 'person');
humanBboxes = bboxes(humanIdx, :);

% Count the number of humans
numHumans = sum(humanIdx);

% Display the results
detectedImg = insertObjectAnnotation(img, 'rectangle', humanBboxes, 'Human');
imshow(detectedImg);
title(['Number of Humans Detected: ', num2str(numHumans)]);

- YOLO v2 Network: This example uses a pre-trained YOLO v2 network, which is efficient for real-time object detection.

This approach provides a straightforward method to distinguish and count humans using MATLAB, leveraging powerful pre-trained models for object detection.

[Walter Roberson]

Consider Dr Who episodes. In the early days, nearly all of the Daleks were humans inside of Dalek shells. In some of the much later episodes, some of the more background Daleks were robots.

Now, how is your image processing going to be able to reliably distinguish between the Daleks that have humans inside, from the Daleks that are robots?