Label Efficient Lifelong Multi-View Broiler Detection

Thorsten Cardoen, Sam Leroux, Pieter Simoens; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5393-5402

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.