Real-Time System for Tracking and Classification of Pedestrians and Bicycles

Abstract

Data on pedestrian and bicycle volumes are necessary for transportation planning, infrastructure design, and traffic management. Nevertheless, such data cannot be collected directly by the commonly used detectors (e.g., inductive loops, sonar, and microwaves). In this study, a pedestrian and bicycle tracking and classification system was developed to detect pedestrians and bicycles with a video camera. This system contained six modules: a video flow capture module, a movement detection module, a shadow removal module, a feature extraction module, a tracking module, and a classification module. The Gaussian mixture model was used to extract moving objects from an image sequence. In the tracking module, the most challenging part of this system, the trajectories were obtained by use of a Kalman filter. To identify pedestrians and bicycles, a backpropagation neural network was used in the classification module. Two other simple but effective algorithms were used to alleviate the negative impacts of shadows and occlusions. The system was tested at three sites under different traffic and environmental conditions. It has been confirmed that the accuracy for pedestrian detection was approximately 85% and the count error rate was less than 13% for bicycles at all test sites. The proposed system is a feasible alternative for the collection of data for nonmotorized travel modes.

Wi-CL: Low-Cost WiFi-Based Detection System for Nonmotorized Traffic Travel Mode Classification

“…Te second step determines which input to choose. Tis step has two substeps, as shown in equations ( 11) and (12).…”

“…However, collecting travel data from pedestrians and cyclists on streets, sidewalks, and public trails is a considerable challenge [11]. In addition, widely used vehicle sensors (such as induction loops, cameras, ultrasonic sensors, and radars) sufer from several problems, including high installation and maintenance costs and inefciencies in pedestrian and cyclist detection and tracking, because pedestrians and cyclists usually have weak behavioral norms, in contrast to motor vehicles, which are restricted by lane rules [12]. Tus, automatic nonmotorized trafc data collection and mode detection techniques should be developed and applied in practical scenarios.…”

Quality Counts for Pedestrians and Bicyclists

“…A pedestrian and bicycle tracking and classification system tested in China proved to be 85% accurate (7). The system used video and image processing to correctly detect and classify types of users.…”

Computer vision supported pedestrian tracking: A demonstration on trail bridges in rural Rwanda

“…A variety of technologies and analysis methods have been deployed and validated to count pedestrians, bicycles and vehicles crossing bridges and other transportation infrastructure. These methods include in-person observational counting [ 4 , 5 ], motion-activated counters [ 4 ], magnetic inductive loops [ 6 ], infrared light beams, pressure pads, thermal cameras [ 7 ], and digital video [ 8 , 9 ] and imagery analysis [ 10 ].…”