  Multi-view Detection (MVD) is highly effective for occlusion reasoning in a
crowded environment. While recent works using deep learning have made
significant advances in the field, they have overlooked the generalization
aspect, which makes them impractical for real-world deployment. The key novelty
of our work is to formalize three critical forms of generalization and propose
experiments to evaluate them: generalization with i) a varying number of
cameras, ii) varying camera positions, and finally, iii) to new scenes. We find
that existing state-of-the-art models show poor generalization by overfitting
to a single scene and camera configuration. To address the concerns: (a) we
propose a novel Generalized MVD (GMVD) dataset, assimilating diverse scenes
with changing daytime, camera configurations, varying number of cameras, and
(b) we discuss the properties essential to bring generalization to MVD and
propose a barebones model to incorporate them. We perform a comprehensive set
of experiments on the WildTrack, MultiViewX, and the GMVD datasets to motivate
the necessity to evaluate the generalization abilities of MVD methods and to
demonstrate the efficacy of the proposed approach. The code and the proposed
dataset can be found at https://github.com/jeetv/GMVD
