Alright folks, let me walk you through this cool little project I’ve been tinkering with – a foot traffic counter with predictive analytics. Sounds fancy, right? Well, it’s actually not that bad once you get your hands dirty.
The Idea:
So, the basic idea was to build something that could count how many people walk into a store (or any space, really) and then use that data to predict future traffic. Why? Well, for businesses, this kind of info is gold. They can optimize staffing, plan inventory, and all that jazz.
Getting Started: The Hardware
- Camera: I grabbed a cheap USB camera off Amazon. Nothing special, just something that could stream video.
- Raspberry Pi: An old Raspberry Pi 4 I had lying around. This is the brains of the operation.
- Some wires and stuff: To hook everything up.
The Software:
- Python: My language of choice. It’s easy to use and has tons of libraries for this kind of thing.
- OpenCV: For video processing. This is where the magic happens.
- TensorFlow/Keras: For the predictive analytics part. We need something to build a model.
- Some other libraries: Like `datetime`, `csv`, and `scikit-learn`.
The Process: Step-by-Step
- Setting up the Pi: I installed the latest Raspberry Pi OS, enabled the camera, and made sure everything was updated. It’s pretty straightforward.
- Getting the Camera Feed: This was the first hurdle. I used OpenCV to grab the video stream from the USB camera. It took a bit of fiddling to get the resolution right and make sure the frame rate was decent.
- Motion Detection: The trick here is to detect when someone walks past. I used a simple background subtraction method. Basically, you take an average of a few frames to create a background image. Then, compare each new frame to the background. If there’s a significant difference (i.e., motion), you mark it as a potential person.
- Counting People: This is where it gets a bit more complex. I used OpenCV to draw bounding boxes around the moving objects and then tracked them as they moved across the frame. If a bounding box crossed a predefined “threshold line,” I incremented the count. It’s not perfect, but it works surprisingly well. There were definitely some false positives (and negatives), but overall, it was good enough.
- Data Logging: I logged the foot traffic count every hour to a CSV file. This gave me a time series dataset to work with.
- Predictive Analytics: Now for the fun part! I used TensorFlow and Keras to build a simple time series forecasting model. I fed the model the historical foot traffic data, and it learned to predict future traffic. I used a Recurrent Neural Network (RNN) for this, which is pretty good at handling time-based data.
- Testing and Tweaking: I tested the system in a real-world setting (my garage) and tweaked the parameters until it was reasonably accurate. This involved adjusting the motion detection sensitivity, the threshold line position, and the model’s hyperparameters.
The Code (Simplified):
I’m not going to paste all the code here, but here’s a rough idea of what it looks like:
Motion Detection:
import cv2
cap = *(0)
background = None
while True:
ret, frame = *()
gray = *(frame, *_BGR2GRAY)
gray = *(gray, (21, 21), 0)
if background is None:
background = *().astype("float")
continue
*(gray, background, 0.5)
delta = *(gray, *(background))
thresh = *(delta, 25, 255, *_BINARY)[1]
thresh = *(thresh, None, iterations=2)
contours, _ = *(*(), *_EXTERNAL, *_APPROX_SIMPLE)
for contour in contours:
if *(contour) < 500:
continue
(x, y, w, h) = *(contour)
*(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
*("Frame", frame)
key = *(1) & 0xFF
if key == ord("q"):
break
Predictive Analytics (Keras):
import tensorflow as tf
from tensorflow import keras
import numpy as np
# Sample data (replace with your actual data)
data = *([10, 12, 15, 18, 20, 22, 25, 28, 30, 32])
# Prepare data for RNN
def prepare_data(data, n_steps):
X, y = [], []
for i in range(len(data) - n_steps):
*(data[i:(i + n_steps)])
*(data[i + n_steps])
return *(X), *(y)
n_steps = 3
X, y = prepare_data(data, n_steps)
X = *((*[0], *[1], 1))
# Build the RNN model
model = *([
*(50, activation='relu', input_shape=(n_steps, 1)),
*(1)
*(optimizer='adam', loss='mse')
# Train the model
*(X, y, epochs=200, verbose=0)
# Make a prediction
x_input = *([data[-3:]])
x_input = x_*((1, n_steps, 1))
yhat = *(x_input, verbose=0)
print("Predicted:", yhat)
Challenges and Lessons Learned:
- Lighting: Consistent lighting is crucial for accurate motion detection. Shadows and changes in lighting can trigger false positives.
- Occlusion: When people walk close together, it can be difficult to distinguish them as separate entities.
- Model Accuracy: The predictive model is only as good as the data you feed it. The more data you have, the better the predictions will be.
- Real-time Processing: Getting the system to run smoothly in real-time on a Raspberry Pi was a challenge. I had to optimize the code and reduce the frame rate to keep up.
Final Thoughts:
This was a fun and challenging project. It’s amazing how much you can do with a few lines of code and some cheap hardware. I learned a lot about video processing, machine learning, and the importance of data quality. While it’s not perfect, it’s a solid proof of concept and something I can definitely build on in the future. Maybe I’ll add some object recognition next time around!
Hope this was helpful or at least mildly entertaining! Let me know if you have any questions or want to dive deeper into any specific part of the project.