Vehicle Detection and Tracking from UAV Imagery via Cascade Classifier

Traffic monitoring plays a vital role in the current world. Previously, stationary data collectors such as video cameras and induction loops were employed for this task. However, the availability of unmanned aerial vehicles (UAV) has opened up new horizons for this task and numerous research projects are being conducted in this field. But object detection and tracking become a challenging task in the case of aerial images due to the presence of high density of objects, challenging view angles, different illumination changes, and varying altitudes of the drone. In this paper, we propose a method for detecting vehicles and also tracking them through the use of cascade classifier and centroid tracking. We have also incorporated georeferencing and coregistration of acquired images and then proceeded on to extract lanes. After segmenting out the region of interest, we proceeded with the detection and tracking tasks.