Moving Object Detection for Event-based Vision using k-means Clustering
- URL: http://arxiv.org/abs/2109.01879v1
- Date: Sat, 4 Sep 2021 14:43:14 GMT
- Title: Moving Object Detection for Event-based Vision using k-means Clustering
- Authors: Anindya Mondal, Mayukhmali Das
- Abstract summary: Moving object detection is a crucial task in computer vision.
Event-based cameras are bio-inspired cameras that work by mimicking the working of the human eye.
In this paper, we investigate the application of the k-means clustering technique in detecting moving objects in event-based data.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Moving object detection is a crucial task in computer vision. Event-based
cameras are bio-inspired cameras that work by mimicking the working of the
human eye. These cameras have multiple advantages over conventional frame-based
cameras, like reduced latency, HDR, reduced motion blur during high motion, low
power consumption, etc. However, these advantages come at a high cost, as
event-based cameras are noise sensitive and have low resolution. Moreover, the
task of moving object detection in these cameras is difficult, as event-based
sensors capture only the binary changes in brightness of a scene, lacking
useful visual features like texture and color. In this paper, we investigate
the application of the k-means clustering technique in detecting moving objects
in event-based data. Experimental results in publicly available datasets using
k-means show significant improvement in performance over the state-of-the-art
methods.
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