Label Efficient Lifelong Multi-View Broiler Detection

Abstract

Broiler localization is crucial for welfare monitoring, particularly in identifying issues such as wet litter. We focus on multi-camera detection systems since multiple viewpoints not only ensure comprehensive pen coverage but also reduce occlusions caused by lighting, feeder and drinking equipment. Previous multi-view detection studies localize subjects either by aggregating ground plane projections of single-view predictions or by developing end-to-end multi-view detectors capable of directly generating predictions. However, single-view detections may suffer from reduced accuracy due to occlusions, and obtaining ground plane labels for training end-to-end multi-view detectors is challenging. In this paper, we combine the strengths of both approaches by using the readily available aggregated single-view detections as labels for training a multi-view detector. Our approach alleviates the need for hard-to-acquire ground-plane labels. Through experiments on a real-world broiler dataset, we demonstrate the effectiveness of our approach.