Fast-moving object counting with an event camera
Kamil Bialik, Marcin Kowalczyk, Krzysztof Blachut, Tomasz, Kryjak

TL;DR
This paper demonstrates that event cameras can effectively count fast-moving objects like falling grains in real-time, offering advantages such as low latency and robustness in various lighting conditions, suitable for industrial applications.
Contribution
The paper introduces a real-time counting algorithm using event cameras for fast-moving objects, validated through practical experiments and control system integration.
Findings
Event cameras enable accurate counting of fast-moving objects.
The system operates in real-time with low latency.
Potential for industrial applications in object counting.
Abstract
This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects - in this case, falling corn grains. These type of cameras transmit information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, correct operation in different lighting conditions, as well as very low power consumption. The proposed counting algorithm processes events in real time. The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of grains falling to be adjusted. The objective of the control system with a PID controller was to maintain a constant average number of falling objects. The proposed solution was subjected to a series of tests to determine the correctness of the developed method operation. On their basis, the validity…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks and Applications · Advanced Memory and Neural Computing
