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#HOW TO GET MATLAB R2015A TO SHOW FIGURES DURING CALCULATION UPDATE#
#HOW TO GET MATLAB R2015A TO SHOW FIGURES DURING CALCULATION CODE#

This can be accomplished using the Simulink and the Simulink Support Package for Raspberry Pi hardware. Once the algorithm is developed, we can deploy the algorithm to the hardware, and it will run without being connecting to Simulink.

The algorithm may not count partially occluded cars.

If there is abrupt change in lighting condition, the algorithm takes couple of seconds to readjust itself.

If there is traffic jam on the road segment, cars at or near a standstill become part of background and may not be properly counted.

Because we are not using a night-vision camera, the car-counting algorithm may not give an accurate result at nighttime.

In the following situations, the algorithm might not work as expected: To avoid this situation, the algorithm would need to be modified to track cars as they enter and leave the region of interest. As a result, under certain high-volume traffic conditions, such as when traffic is stopped or moving slowly, it may be possible to count some cars twice. The count value is an estimate because the algorithm used does not track the cars. We send both values to ThingSpeak (channel 38629), and store the eastbound value in Field 2 and the westbound value in Field 1. In this block, we send a value for eastbound vehicle count and westbound vehicle count. Lastly the data is sent to the data aggregator using the ThingSpeak Write block. It then counts the cars above and below that line. This block breaks up the region of interest into two sub sections along the highway median. It is then analyzed using the Blob Analysis block, which computes centroids of the blob containing the cars.įinally, the Car Counter Block estimates the number of cars traveling in each direction during the 15 second interval. The foreground mask is then post-processed using Median Filter to remove unwanted noise. The vision.ForegroundDetector estimates the background using Gaussian mixture models and produces a foreground mask highlighting foreground objects-in this case, moving cars. Next the vision.ForegroundDetector estimates the foreground pixels of the video sequence captured from the stationary webcam. Moving from left to right in the model, we can see the USB webcam connected to the Raspberry Pi captures video with a region of interest selected. In this mode, Simulink gathers the video stream from the Raspberry Pi, and the user can view the video on an external monitor using the SDL Video Display block while the algorithm is running, as shown here. To develop the algorithm, we used the external mode capability of Simulink.

The Simulink model for the car-counting algorithm is shown below.

#HOW TO GET MATLAB R2015A TO SHOW FIGURES DURING CALCULATION CODE#

In this example, Simulink generates code that runs on the Raspberry Pi 2. Simulink is a modeling environment that can to automatically generate code that can run on an embedded controller. To develop the car counting algorithm, we used Simulink, Image Processing Toolbox, Computer Vision System Toolbox and the Simulink Support Package for Raspberry Pi Hardware.

#HOW TO GET MATLAB R2015A TO SHOW FIGURES DURING CALCULATION UPDATE#

We then send the count value to the data aggregator at an update rate of once every 15 seconds, the maximum data rate allowed by ThingSpeak. 5 V 2 A switching Power Supply with MicroUSB cableīecause we did not want to send high-bandwidth video images to the cloud, we chose to detect the vehicles at the edge using the processor on the Raspberry Pi 2.Raspberry Pi 2 Model B ARM v7 with 1 GB RAM.This project stores data in channel 38629 on ThingSpeak.Īnalytics are everywhere in the Internet of Things (IoT) occurring at: We deploy a car-counting algorithm to the Raspberry Pi device, and we analyze and visualize the traffic patterns with ThingSpeak, a data aggregator. In this IoT project, we use a Raspberry Pi, a web cam and ThingSpeak to count cars on a busy highway. Online Analysis: Calculating and Scheduling MATLAB Code inside ThingSpeak.Weekend observations for Sunday, July 26.Weekend observations for Saturday, July 25.Downsample into 48 Bins of Approximately 30 Minute Chunks of Data and Find Peaks.Check Time Difference Between Samples to Verify How Much Data is Missing.Taking a Deeper Look at Individual Days during the Week and on the Weekend.Looking at Weekly Sums for Eastbound and Westbound Traffic and Plotting.Looking at Traffic Volume as a Histogram.

Deploying the Algorithm to the Hardware.