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 article, we present a simple, yet effective method for tracking pedestrian and bicycle objects in a relatively large surveillance area, using ordinary un-calibrated 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.

have access to the Internet. Most personal electronic devices, such as personal digital assistants, cell phones, and smartphones, have embedded Bluetooth and Wi-Fi modules that can communicate with other peripheral electronic devices and the Internet.Analogous to most communicating devices, each Bluetooth and WiFi device has a globally unique 48-bit media access control (MAC) address. This unique identifier is what provides potential for an easy means of obtaining travel characteristics of a given humanpopulated network (2, 4). Not only can the overall population of the system be estimated, but the travel times, routing choices, and interactions can be analyzed to provide an overall understanding of spatial and temporal patterns (1). Moreover, device ID-based reidentification approaches are as universal as the communication protocols that power them and the devices those protocols use. This allows for a standard method of evaluation of urban core travel across the globe.Relying on mobile devices for acquiring travel data also provides an opportunity to pursue information parity for sustainable modes. For nearly a century, transportation metrics have favored motorized means by focusing on measures like mobility (number of miles traveled) and vehicle volume counts, which do not describe the entirety of the transportation system (5). As a result, little attention has been paid to pedestrian and bicycle facilities until recent efforts have highlighted these modes as sustainable alternatives. The development of information-gathering techniques that are based on mobile devices allows communities to begin to collect data about these sustainable modes grounded on individuals and not on their vehicles. However, the lack of either a framework for data collection or a systematic approach stymies these efforts.This paper aims to create a precedent for monitoring pedestrian movements by means of static Bluetooth sensors. Basic parameters, such as travel time and sample rates, are discussed and presented for two study sites, one in Montreal, Quebec, Canada, and one in Seattle, Washington. The remainder of the paper begins with an overview of existing efforts, which is followed by a discussion of the methodology used in the study. Results from the two study sites are then described and discussed, and they are followed by concluding remarks.

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.

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.

No abstract