The bus-stop project is a vision-based method I developed to automatically detect individuals loitering about inner-city bus stops. This project is useful because prolonged loitering, hanging around a bus stop much longer than is necessary to catch a bus, is indicative of drug dealing behavior. Minneapolis Metro Transit is concerned about drug dealing at their bus stops because it compromises safety, and discourages citizens from using them.
Using a stationary camera view of a bus stop, pedestrians are segmented and tracked throughout the scene. The system takes snapshots of individuals when a clean, non-obstructed view of a pedestrian is found. The snapshots are then used to classify the individual images into a database, using an appearance-based method. The features used to correlate individual images are based on short-term biometrics, which are changeable but stay valid for short periods of time. This system uses clothing color. A linear discriminant method is applied to the color information to enhance the differences and minimize similarities between the different individuals in the feature space. To determine if a given individual is loitering, timestamps collected with the snapshots in their corresponding database class can be used to judge how long an individual has been present. An experiment was performed using
a 30 minute video of a busy bus stop with six individuals loitering about it. Our results show that the system successfully classifies images of all six individuals as loitering.
- Nathaniel Bird, Osama Masoud, Nikolaos Papanikolopoulos, and Aaron Isaacs, “Detection of loitering individuals in public transportation areas,” IEEE Transactions on Intelligent Transportation Systems, vol. 6, no. 2, pp. 167-177, June 2005.
- Guillaume Gasser, Nathaniel Bird, Osama Masoud, and Niolaos Papanikolopoulos, “Human activities monitoring at bus stops,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 1, pp. 90-95, April 2004.