2,544 research outputs found
An effective video analysis method for detecting red light runners
This paper presents a novel method for automatic red light runner detection on a video, which is fundamentally different from the concept of conventional red light camera systems. In principle, it extracts the state of the traffic lights and vehicle motions without any physical or electronic interconnections to the traffic light control system or the buried loop detectors. Purely from the video, the new method first constructs a traffic light sequence and then it estimates vehicle motions beyond the stop line while the light is red. In the former, the spatial and temporal relationships of individual traffic lights are utilized. In the latter, the concept of virtual loop detector has been introduced to emulate the physical loop detectors. A prototype was implemented based on this method and was tested in a number of field trials. The results show that the new method is able to detect multiple red light runners in multiple lanes. It is also capable of tolerating a number of hostile but realistic situation such as: 1) minimum number of traffic light; 2) pseudomotions due to shadows; 3) poor contrast; 4) pedestrian motions; and 5) turning vehicles.published_or_final_versio
Penalty Generation System and Traffic Violation Detection at a Street Intersection
Due to urbanization and industrialization there is rapid increase in number of vehicles running on the roads. This has resulted in frequent traffic jams, signal violation and accidents at the street intersection. Also it is not possible to assign a traffic police at each and every street. This generates the need for proper penalty system depending purely on video processing techniques
The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race
Recent studies in social media spam and automation provide anecdotal
argumentation of the rise of a new generation of spambots, so-called social
spambots. Here, for the first time, we extensively study this novel phenomenon
on Twitter and we provide quantitative evidence that a paradigm-shift exists in
spambot design. First, we measure current Twitter's capabilities of detecting
the new social spambots. Later, we assess the human performance in
discriminating between genuine accounts, social spambots, and traditional
spambots. Then, we benchmark several state-of-the-art techniques proposed by
the academic literature. Results show that neither Twitter, nor humans, nor
cutting-edge applications are currently capable of accurately detecting the new
social spambots. Our results call for new approaches capable of turning the
tide in the fight against this raising phenomenon. We conclude by reviewing the
latest literature on spambots detection and we highlight an emerging common
research trend based on the analysis of collective behaviors. Insights derived
from both our extensive experimental campaign and survey shed light on the most
promising directions of research and lay the foundations for the arms race
against the novel social spambots. Finally, to foster research on this novel
phenomenon, we make publicly available to the scientific community all the
datasets used in this study.Comment: To appear in Proc. 26th WWW, 2017, Companion Volume (Web Science
Track, Perth, Australia, 3-7 April, 2017
On the Feasibility of Social Network-based Pollution Sensing in ITSs
Intense vehicular traffic is recognized as a global societal problem, with a
multifaceted influence on the quality of life of a person. Intelligent
Transportation Systems (ITS) can play an important role in combating such
problem, decreasing pollution levels and, consequently, their negative effects.
One of the goals of ITSs, in fact, is that of controlling traffic flows,
measuring traffic states, providing vehicles with routes that globally pursue
low pollution conditions. How such systems measure and enforce given traffic
states has been at the center of multiple research efforts in the past few
years. Although many different solutions have been proposed, very limited
effort has been devoted to exploring the potential of social network analysis
in such context. Social networks, in general, provide direct feedback from
people and, as such, potentially very valuable information. A post that tells,
for example, how a person feels about pollution at a given time in a given
location, could be put to good use by an environment aware ITS aiming at
minimizing contaminant emissions in residential areas. This work verifies the
feasibility of using pollution related social network feeds into ITS
operations. In particular, it concentrates on understanding how reliable such
information is, producing an analysis that confronts over 1,500,000 posts and
pollution data obtained from on-the- field sensors over a one-year span.Comment: 10 pages, 15 figures, Transaction Forma
The Effects of Computational Resources on Flaky Tests
Flaky tests are tests that nondeterministically pass and fail in unchanged
code. These tests can be detrimental to developers' productivity. Particularly
when tests run in continuous integration environments, the tests may be
competing for access to limited computational resources (CPUs, memory etc.),
and we hypothesize that resource (in)availability may be a significant factor
in the failure rate of flaky tests. We present the first assessment of the
impact that computational resources have on flaky tests, including a total of
52 projects written in Java, JavaScript and Python, and 27 different resource
configurations. Using a rigorous statistical methodology, we determine which
tests are RAFT (Resource-Affected Flaky Tests). We find that 46.5% of the flaky
tests in our dataset are RAFT, indicating that a substantial proportion of
flaky-test failures can be avoided by adjusting the resources available when
running tests. We report RAFTs and configurations to avoid them to developers,
and received interest to either fix the RAFTs or to improve the specifications
of the projects so that tests would be run only in configurations that are
unlikely to encounter RAFT failures. Our results also have implications for
researchers attempting to detect flaky tests, e.g., reducing the resources
available when running tests is a cost-effective approach to detect more flaky
failures.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Vehicle-type identification through automated virtual loop assignment and block-based direction biased motion estimation
This paper presents the concept of automated virtual loop assignment and loop-based motion estimation in vehicle-type identification. A major departure of our method from previous approaches is that the loops are automatically assigned to each lane; the size of virtual loops is much smaller for estimation accuracy; and the number of virtual loops per lane is large. Comparing this with traditional ILD, there are a number of advantages. First, the size and number of virtual loops may be varied to fine-tune detection accuracy and fully utilize computing resources. Second, there is no failure rate associated with the virtual loops and installation and maintenance cost can be kept to a minimum. Third, virtual loops may be re-allocated anywhere on the frame, giving flexibility in detecting different parameters.published_or_final_versio
Artificial intelligence enabled automatic traffic monitoring system
The rapid advancement in the field of machine learning and high-performance computing have highly augmented the scope of video-based traffic monitoring systems. In this study, an automatic traffic monitoring system is proposed that deploys several state-of-the-art deep learning algorithms based on the nature of traffic operation. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to track congestion, detect traffic anomalies and tabulate vehicle counts. To monitor traffic queues, this study implements a Mask region-based convolutional neural network (Mask R-CNN) that predicts congestion using pixel-level segmentation masks on classified regions of interest. Similarly, the model was used to accurately extract traffic queue-related information from infrastructure mounted video cameras. The use of infrastructure-mounted CCTV cameras for traffic anomaly detection and verification is further explored. Initially, a convolutional neural network model based on you only look once (YOLO), a popular deep learning framework for object detection and classification is deployed. The following identification model, together with a multi-object tracking system (based on intersection over union -- IOU) is used to search for and scrutinize various traffic scenes for possible anomalies. Likewise, several experiments were conducted to fine-tune the system's robustness in different environmental and traffic conditions. Some of the techniques such as bounding box suppression and adaptive thresholding were used to reduce false alarm rates and refine the robustness of the methodology developed. At each stage of our developments, a comparative analysis is conducted to evaluate the strengths and limitations of the proposed approach. Likewise, IOU tracker coupled with YOLO was used to automatically count the number of vehicles whose accuracy was later compared with a manual counting technique from CCTV video feeds. Overall, the proposed system is evaluated based on F1 and S3 performance metrics. The outcome of this study could be seamlessly integrated into traffic system such as smart traffic surveillance system, traffic volume estimation system, smart work zone management systems, etc.by Vishal MandalIncludes bibliographical reference
SignalGuru: Leveraging mobile phones for collaborative traffic signal schedule advisory
While traffic signals are necessary to safely control competing flows of traffic, they inevitably enforce a stop-and-go movement pattern that increases fuel consumption, reduces traffic flow and causes traffic jams. These side effects can be alleviated by providing drivers and their onboard computational devices (e.g., vehicle computer, smartphone) with information about the schedule of the traffic signals ahead. Based on when the signal ahead will turn green, drivers can then adjust speed so as to avoid coming to a complete halt. Such information is called Green Light Optimal Speed Advisory (GLOSA). Alternatively, the onboard computational device may suggest an efficient detour that will save the driver from stops and long waits at red lights ahead.
This paper introduces and evaluates SignalGuru, a novel software service that relies solely on a collection of mobile phones to detect and predict the traffic signal schedule, enabling GLOSA and other novel applications. Our SignalGuru leverages windshield-mounted phones to opportunistically detect current traffic signals with their cameras, collaboratively communicate and learn traffic signal schedule patterns, and predict their future schedule.
Results from two deployments of SignalGuru, using iPhones in cars in Cambridge (MA, USA) and Singapore, show that traffic signal schedules can be predicted accurately. On average, SignalGuru comes within 0.66s, for pre-timed traffic signals and within 2.45s, for traffic-adaptive traffic signals. Feeding SignalGuru's predicted traffic schedule to our GLOSA application, our vehicle fuel consumption measurements show savings of 20.3%, on average.National Science Foundation (U.S.). (Grant number CSR-EHS-0615175)Singapore-MIT Alliance for Research and Technology Center. Future Urban Mobilit
SuperFight I: the battle to understand a space through the behaviour of its occupants
This dissertation describes an interaction design project which investigates whether the symbiosis of physical and digital environments might be used to create a stronger sense of ‘place’ for the occupants of the physical space. Sensing technology, implicit interaction, ambient interfaces, game strategy and a network connection were combined in an attempt to increase participants' awareness of their physical actions and location. The principles and theory underpinning this project are discussed, after which a list of criteria for an interactive system designed for public spaces is drawn up. The design of SuperFight I is described and evaluated in relation to this theoretical background. Finally suggestions are made for future areas of research that might be undertaken in order to develop the system further
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