DOI: 10.14704/nq.2022.20.8.NQ44701

OBJECT DETECTION USING EVENT BASED CLUSTERING TECHNIQUE

C. Saraswathy M.E., V. Vijaykarthikeyan

Abstract

Clustering is crucial for many computer vision applications such as robust tracking, object detection and segmentation. Object detection/recognition finds its application in drones, autonomous driving, and so on. This work presents a real-time clustering technique that takes advantage of the unique properties of event-based vision sensors. Thus, this approach redefines the well-known clustering method using asynchronous events instead of conventional frames. Clustering accuracy reducing the computational cost by 88% compared to the frame-based method. The clustering achieved a consistent number of clusters along time. Event-based algorithm has been used for this cluster detection and we evaluated our method on a Verilog platform. This cluster detection mechanism is reliable, having less complexity on comparing with other image processing techniques.

Keywords

Event-based processing, VHDL, Abnormal event detection, Linear feedback shift register( LFSR), cluster detection, Object Tracking, Cluster Object Tracker (COT).

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