Virtual context-based multi-camera vehicle tracking

Vision-based vehicle detection and tracking systems are becoming essential utilities for extracting statistical traffic information on roads. However, single camera causes data loss due to occlusion or insufficient shooting area. The camera position is also an important factor for healthy observation. There are panoramic cameras or fish-eye cameras to observe wider scene, however they do not solve the occlusion problem. In this study we propose a new concept "virtual context" which is created by merging multiple camera video streams intelligently. This context can be fed to any object detection and tracking algorithm such as YOLO.