Yegor Malinovskiy

Video-based vehicle detection and tracking using spatio-temporal maps

Surveillance video cameras have been increasingly deployed along roadways over the past decade. Automatic traffic data collection through surveillance video cameras is highly desirable; however, sight-degrading factors and camera vibrations make it an extremely challenging task. In this paper, a computer-vision–based algorithm for vehicle detection and tracking is presented, implemented, and tested. This new algorithm consists of four steps: user initialization, spatiotemporal map generation, strand analysis, and vehicle tracking. It relies on a single, environment-insensitive cue that can be easily obtained and analyzed without camera calibration. The proposed algorithm was implemented in Microsoft Visual C++ using OpenCV and Boost C++ graph libraries. Six test video data sets, representing a variety of lighting, flow level, and camera vibration conditions, were used to evaluate the performance of the new algorithm. Experimental results showed that environmental factors do not significantly impact the detection accuracy of the algorithm. Vehicle count errors ranged from 8% to 19% in the tests, with an overall average detection accuracy of 86.6%. Considering that the test scenarios were chosen to be challenging, such test results are encouraging.

Model-Free Video Detection and Tracking of Pedestrians and Bicyclists

Pedestrian and bicycle monitoring is quickly becoming an avid area of interest as information regarding pedestrian and bicycle flow is needed not only for developing competent access to particular urban corridors and trails, but also for system optimization scenarios, such as transit system operations and intersection controls. In this paper, the authors present a simple, yet effective method for tracking pedestrian and bicycle objects in a relatively large surveillance area, using ordinary uncalibrated video images. Object extraction is accomplished via background subtraction, while tracking is accomplished through an inherent characteristic cost function. Composite objects are used as a means of dealing with occlusions. The algorithm is implemented using Microsoft Visual C# and was tested on numerous scenes of varying complexity, resulting in an average count rate of 92.7% at the specified checkpoints.

Video-Based Monitoring of Pedestrian Movements at Signalized Intersections

Pedestrian and cyclist crossing characteristics are important for the design of urban intersections and signalized crossings. Parameters such as waiting time, crossing time, and arrival rate are key variables for describing pedestrian characteristics and improving crossing designs and signal timing plans. Manually collecting such data is often extremely labor intensive. Therefore, an automated computer-vision-based approach is introduced for collecting these parameters in real time with ordinary video cameras. Broadly defined pedestrian objects, including bicyclists and other nonmotorized modes, are extracted by means of the background subtraction technique and tracked through an inherent cost characteristic function in conjunction with an α-β-filter. The waiting-zone concept introduced helps provide robust pedestrian tracking initialization and parameter extraction. The proposed approach is implemented in a pedestrian tracking (PedTrack) system by using Microsoft Visual C++. Tested with real video data from three study sites, this system was proved to be effective and about 80% of pedestrian crossing events were successfully detected. PedTrack shows the potential to be a great data collection tool for nonmotorized object movements at intersections.

Analysis of Pedestrian Travel with Static Bluetooth Sensors

Travel evaluation metrics have been historically biased toward motorized modes, which dominate land transportation choices and are partially responsible for numerous environmental and health issues facing society today. Encouraging active travel solutions is seen as a means of improving the sustainability, health, and cohesiveness of a community. Unfortunately, information about volume, trip origin and destination, travel time, and personal interactions is difficult to obtain because of a lack of sensor infrastructure and unrestricted movement of these modes. Therefore, information is often limited to annual surveys and model estimates that are insufficient to address the increasing needs of sustainable planning and large-scale behavior studies. An automated, cost-effective approach to acquiring pedestrian data is desirable. The emergence of Bluetooth sensors as a means of gathering travel time data for traffic analysis presents an opportunity to use the same technology for pedestrian travel analysis. However, because people generally carry more Bluetooth devices in their vehicles than they do on their person, making representative sample sizes is a challenge. A study of pedestrian detection with Bluetooth technology is presented at two sites (Montreal, Quebec, Canada, and Seattle, Washington) to investigate the feasibility of Bluetooth technology for pedestrian studies. The results indicate that, given sufficient populations, high-level trend analysis can provide insights into pedestrian travel behavior.

Beyond Context-Sensitive Solutions: Using Value-Sensitive Design to Identify Needed Transit Information Tools

Although the practice of Context-Sensitive Solutions instructs transportation designers to respect community values, the focus is typically on the surroundings of the project rather than the direct and indirect stakeholders who will be affected by a solution. Therefore, the team designing the OneBusAway transit traveler information system turned to the Value-Sensitive Design (VSD) process from information and computer science to help determine what transit rider information tools to build next. Through conceptual, empirical, and technical investigations, the OneBusAway team has developed a list of potential transit information tools and begun to prioritize projects based on the needs of riders of all types and effects on indirect stakeholders. The use of VSD has helped guide the use of limited resources, so that they are spent meeting the actual needs of the larger public-transit-using community. The principles of VSD can be applied throughout the transportation industry, especially when considering broader transportation planning goals.