Vehicle tracking improved by using indirect data from third-party sensors
Beyond Direct Sensing: Harnessing Indirect Observations from Third-Party Sensors in Vehicle Tracking
Machine Learning
Summary
Tracking vehicles is important but hard when the usual sensors are hard to access or only give limited information. The authors looked at whether weaker, indirect clues from other sensors could help fill in the gaps when direct data is missing. They created GrayTrack, which combines both strong and weak signals to follow vehicles better. Testing this system showed it greatly improved how accurately vehicles could be tracked and reduced losing track of them. This work shows that even partial or anonymous observations can boost tracking when combined smartly.
What this means in practice
- •For urban mobility planners: Enhance traffic monitoring systems by integrating anonymous movement data from third-party sensors to fill gaps where direct vehicle data is unavailable.
- •For public safety operators: Improve vehicle tracking during investigations by combining sparse official sensor data with indirect observations from external cameras.
Authors
Gaofeng Dong, Vamsi Eyunni, Pragya Sharma, Kang Yang, Mani Srivastava
Abstract
Vehicle tracking is fundamental to applications ranging from urban mobility and public safety to security and defense. Conventional tracking relies on direct access to sensors that provide strong observations such as vehicle identity and location. In practice, however, factors such as ownership, privacy, cost, and operational constraints may limit directly accessible sensors, leaving sparse observations and long tracking gaps. Meanwhile, many additional third-party sensing assets may be present across the environment but remain inaccessible at the raw-data level, preventing their direct integration into the tracking system. In this work, we investigate whether weak, indirect observations with uncertain spatial and temporal cues can complement sparse direct sensing for vehicle tracking. Specifically, we propose GrayTrack, which fuses weak anonymous events with sparse direct observations using a road-constrained particle filter. We build a CARLA-Mininet-WiFi pipeline to evaluate the system under controlled conditions, generating direct observations from accessible cameras and indirect observations from third-party cameras. Our learning-based detector achieves an F1 score of 0.989 for anonymous vehicle passages. Further, incorporating indirect third-party observations reduces trajectory RMSE by 60.1% and catastrophic track loss from 35.8% to 0.3%. These results demonstrate that GrayTrack can effectively exploit weak indirect observations to extend tracking capabilities.