This week, we have been working on getting all our details for our project finalized and documented. This started with presenting and getting feedback from our design review last Tuesday. Now we have been making a few changes, particularly to rerouting, and writing them down for the paper version of the design review due next week. We have also been making progress on the different aspects of our projects’ work, such as the image and video processing, the rerouting and the video recording. We are on schedule and no updates on that end required at the moment.
Arvind Status Update 02/26/22
This week, we have mainly been working on our design review. Therefore, a lot of the work has been spent on ironing out the specifics of our project. So with regard to the image / video processing aspects of the projects, we’ve decided firmly on the data sets, the types of algorithms we wish to use on the images, and the specific type of neural net to use. I have been writing all of this down as part of the report due this week.
I have continued to experiment with the image subtraction and dilation I talked about last week. I am not getting them to work nearly as well as how it is presented in the resource I linked last time, but I’m thinking it will work well enough to be able to start tracking some vehicles. The goal for this week is certainly to get the preprocessing to a stage where I am able to track vehicles in an image by detecting movement from one image to the next.
I also want to go over feedback we got from my design review presentation in today’s meeting, especially with regard to the rerouting aspect of our project as there were a few questions about that that came up. We can use Monday’s group time and meeting with the professor to clarify.
Arvind Status Report- 02/19/2022
This week I mainly worked on experimenting with the Video Processing methods on the traffic intersection data. I am working with the sherbrooke intersection video data in Montreal.
The first thing I did was image differentiation. Essentially you take one frame and subtract it from the following frame. Theoretically, the only differences should be movement from any moving objects- either vehicles of pedestrians. We then apply a thresholding so that only these moving regions are take into considerations going forward in the pipeline. The results looked a little iffy, they certainly needed to be filtered to get a smoother looking object shape. I have been following the advice in this link: https://www.analyticsvidhya.com/blog/2020/04/vehicle-detection-opencv-python/
The idea is to get this preprocessing as good as possible at outlining the boundaries of the vehicles so that they can be classified correctly by the neural network moving forward. It also may be possible to do this without using a neural network, and this may be a route to check out. For example, if we use an intersection where there are no pedestrians- or where the angle is such that it is very obvious and easy to differentiate between a vehicle and a pedestrian- then there may be a good deterministic approach to deciding that a object’s outline is indeed a vehicle.
I am also presenting this week, so I spent some time polishing up the slides we worked on and practice my presenting skills.
Team Status Report – 02/12/22
The most significant risk next few week is not being able to implement a properly working crash detection algorithm. This is simply because this is probably the most challenging part of our project to get working. It also serves as a kind of roadblock as the future components of our project rely on getting this one working well. Thus, we would like to spend a lot of time next week on this. Our contingency plan would be to move forward with an algorithm that may not be working super well, but working well enough to implement future components, and then go back and try to retrain / redo the initial crash detection algorithm. Other than that, we have done a lot of research on the various components of our project, such as the hardware to purchase, the data and neural network / algorithms to use to train for crash detection, and formal / algorithmic ways to think about traffic rerouting. We definitely need to spend some time collecting our thoughts and start implementing what we have in mind. There are no changes to the schedule / future plans.
Arvind’s Status Post – 02/12/22
We are currently in the research / gathering information stage of our project. I have found a research paper that appears to follow a similar process to the one we have planned, where they use computer vision algorithms to do vehicle detection and then track the vehicles’ speeds and positions to determine crashes. This paper could be useful in addition to the data / information my team mates have gathered.
We will need to simulate traffic environments with real hardware. I found this project here: https://create.arduino.cc/projecthub/umpheki/control-your-arduino-from-your-laptop-via-wifi-with-esp13-346702?ref=part&ref_id=8233&offset=25
This project involve an arduino board with a shield that allows it to connect to WiFi. It also uses the wifi communication to control an LED. This is very similar to what we want to simulate, as we will be using LEDs as our “traffic lights.” We think WiFi is the best wireless communication protocol to use, as we will have to communicate from a computer to our Arduino boards as the traffic detection is being run on a remote computer. WiFi makes this connection easy. It also simulates how our system would be potentially implemented in real life, where a traffic light would have to wirelessly communicate with another traffic light farther away. I think purchasing the components of this project and building it would be a good place to start. We can then make modification to suit our purposes better and experiment.
I think we are on track in terms of schedule but do need to start collecting and implementing our ideas. By next week I hope to have started and potentially finished constructing the above project depending on how quickly we can purchase parts.