Clustering-based Object Detection for Low-resolution Video Streaming

Abstract

This paper presents a novel strategy for the detection and tracking of objects in low resolution video sequences. The processing is performed in run-time, considering only few buffered frames. Our approach consists of three main steps: (i) spatial segmentation by means of clustering, (ii) candidate set reduction based on feature extraction (iii) choice of… (More)

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