Identification of vehicle tracks and association to wireless endpoints by multiple sensor modalities

Vehicular positioning technologies enable a broad range of applications and services such as navigation systems, driver assistance systems and self-driving vehicles. However, Global Navigation Satellite Systems (GNSS) do not work in enclosed areas such as parking garages. For these scenarios, a wide range of indoor positioning technologies are available inside the vehicle (internal) and based on infrastructure (external). Based on our previous work, we use off-the-shelf network video cameras to detect the position of moving vehicles within the parking garage in multiple non-overlapping camera views. Towards the goal of using this system as positioning source for vehicles, detected positions need to be transmitted to the communication endpoint in the correct vehicle. The key problem thereby is the association of the externally-observed position to the endpoint in the corresponding vehicle. State-of-the-art tracking-by-detection techniques can differentiate multiple camera-detected vehicles but the generated tracks are anonymous and cannot inherently be associated to the corresponding vehicle. To bridge this gap, we present a tracking-by-identification solution which analyzes vehicle movement patterns by multiple vehicle sensor modalities and compares them with camera-detected tracks to identify the track with the best correlation. The presented approach is based on Kalman Filters and suitable for real-time operation. Test results show that a correct and robust association between endpoints and camera-detected tracks is achieved and that occurring identity switches can be resolved.