Abstract
Instead of wastefully sending entire images at fixed frame rates, neuromorphic vision sensors only transmits the local pixel-level changes caused by movement in a scene at the time they occur. This results in a stream of events, with a latency in the order of micro-seconds. While these sensors offer tremendous advantages in terms of latency and bandwidth, they require new, adapted approaches to computer vision, due to their unique event-based pixel-level output. In this contribution, we propose an online multi-target tracking system utilizing for neuromorphic vision sensors, which is the first neuromorphic vision system in intelligent transportation systems. In order to track moving targets, a fast and simple object detection algorithm using clustering techniques is developed. To make full use of the low latency, we integrate an online tracking-by-clustering system running at a high frame rate, which far exceeds the real-time capabilities of traditional frame based industry cameras. The performance of the system is evaluated using real world dynamic vision sensor data of a highway bridge scenario. We hope that our attempt will motivate further research on neuromorphic vision sensors for intelligent transportation systems.
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Acknowledgments
The research leading to these results has received funding from the European Unions Horizon 2020 Research and Innovation Program under Grant Agreement No. 720270 (HBP SGA1).
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Hinz, G. et al. (2017). Online Multi-object Tracking-by-Clustering for Intelligent Transportation System with Neuromorphic Vision Sensor. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_11
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