The goal of this project Is to develop a passive vision-based sensing system. The system will be capable of monitoring an intersection by observing the vehicle and pedestrian flow, and predicting situations that might give rise to accidents. A single camera looking at nil intersection from an arbitrary position is used. However, for extended applications, multiple cameras will be needed. Some of the key elements are camora calibration, motion tracking, vehicle classification, and situations giving rise to collisions. In this paper, we focus on motion tracking. Motion segmentation is performed using an adoptive background model that models each pixel as a mixture of Gaussians. The method used is similar to the method of Stauffer fit al. for motion segmentation. Tracking of objects is performed by computing the overlap between oriented bounding boxes. The oriented boxes are computed by vector quantization of the blobs in the scene. The principal angles computed during vector quantization along with other cuos of the object are used for classification of detected entities into vehicles and pedestrians.