Stellenbosch University final year project: 2014
Developed by: Andrew de Bruin - 16458826
Drone-based traffic flow estimation and tracking using computer vision
De Bruin, A.; Booysen, M.J.
Traffic flow estimation is a technique by which traffic engineers analyse the extent to which a particular road segment can accommodate traffic. Decisions regarding the need for road upgrades, the installation of speeding cameras and even general security upgrades are made based on these results. Since traffic cameras are usually installed throughout urban areas, exploiting this established infrastructure would allow for the seamless integration of an autonomous traffic flow estimation system. This can be achieved by designing a highly flexible and adaptive system, that would allow for the analyses of traffic footage from various input sources.
The purpose of this project was to design and implement a system that allowed for autonomous traffic flow estimation using computer vision techniques. The system was required to compute traffic flow metrics and upload the information in real time to an online dashboard.
The Mixture of Gaussian (MoG) background subtraction technique was implemented throughout the project as the primary method of vehicle detection. Shadows were detected based on certain chromaticity characteristics, before being subsequently removed from the foreground mask. Relative vehicle velocities were computed based optical flow tracking techniques. Results showed both the vehicle detection as well as the velocity computations to be around 95% accurate under ideal conditions, and around 80% under non-ideal illumination conditions.
A particularly attractive alternative to the static pole mounted traffic cameras, is to use a fully autonomous aircraft. In this way, simple landing platforms could eventually replace expensive traffic cameras. This would allow for the analysis of traffic flow in more rural areas where infrastructure is often non existent. The system designed to automate the aircraft flight control was implemented in the discrete time domain using augmented difference equations. The method of least squares was used to obtain a model of the plant so that appropriate controllers could be designed. The Ziegler-Nichols tuning method was ultimately used to obtain the practical controller parameters. Results obtained during the testing procedure showed that the aircraft was able to land on the designated platform. The inclusion of the automated aircraft is intended to add additional functionality to the primary aim of this project.
The system was designed in a modular fashion, with each submodule contributing additional functionality. The modules were then integrated and thoroughly tested to ensure optimal system performance. Results of the testing showed the system to be fully functional, and proved the concept of autonomous traffic flow estimation to be a viable option.
Music by: "Mark Petrie-Epic Legend"
This site provides links to random videos hosted at YouTube, with the emphasis on random.
The original idea for this site actually stemmed from another idea to provide a way of benchmarking the popularity of a video against the general population of YouTube videos. There are probably sites that do this by now, but there wasn’t when we started out. Anyway, in order to figure out how popular any one video is, you need a pretty large sample of videos to rank it against. The challenge is that the sample needs to be very random in order to properly rank a video and YouTube doesn’t appear to provide a way to obtain large numbers of random video IDs.
Even if you search on YouTube for a random string, the set of results that will be returned will still be based on popularity, so if you’re using this approach to build up your sample, you’re already in trouble. It turns out there is a multitude of ways in which the YouTube search function makes it very difficult to retrieve truly random results.
So how can we provide truly random links to YouTube videos? It turns out that the YouTube programming interface (API) provides additional functions that allow the discovery of videos which, with the right approach, are much more random. Using a number of tricks, combined some subtle manipulation of the space-time fabric, we have managed to create a process that yields something very close to 100% random links to YouTube videos.
YouTube is an American video-sharing website headquartered in San Bruno, California. YouTube allows users to upload, view, rate, share, add to playlists, report, comment on videos, and subscribe to other users. It offers a wide variety of user-generated and corporate media videos. Available content includes video clips, TV show clips, music videos, short and documentary films, audio recordings, movie trailers, live streams, and other content such as video blogging, short original videos, and educational videos. Most content on YouTube is uploaded by individuals, but media corporations including CBS, the BBC, Vevo, and Hulu offer some of their material via YouTube as part of the YouTube partnership program. Unregistered users can only watch videos on the site, while registered users are permitted to upload an unlimited number of videos and add comments to videos. Videos deemed potentially inappropriate are available only to registered users affirming themselves to be at least 18 years old.
YouTube and selected creators earn advertising revenue from Google AdSense, a program which targets ads according to site content and audience. The vast majority of its videos are free to view, but there are exceptions, including subscription-based premium channels, film rentals, as well as YouTube Music and YouTube Premium, subscription services respectively offering premium and ad-free music streaming, and ad-free access to all content, including exclusive content commissioned from notable personalities. As of February 2017, there were more than 400 hours of content uploaded to YouTube each minute, and one billion hours of content being watched on YouTube every day. As of August 2018, the website is ranked as the second-most popular site in the world, according to Alexa Internet, just behind Google. As of May 2019, more than 500 hours of video content are uploaded to YouTube every minute.
YouTube has faced criticism over aspects of its operations, including its handling of copyrighted content contained within uploaded videos, its recommendation algorithms perpetuating videos that promote conspiracy theories and falsehoods, hosting videos ostensibly targeting children but containing violent and/or sexually suggestive content involving popular characters, videos of minors attracting pedophilic activities in their comment sections, and fluctuating policies on the types of content that is eligible to be monetized with advertising.