Very Important Person Localization in Unconstrained Conditions: A New Benchmark

This paper presents a new high-quality dataset for Very Important Person Localization (VIPLoc), named Unconstrained-7k. Generally, existing datasets are: 1) limited in scale; 2) built under simple and constrained conditions, where the number of disturbing non-VIPs is not large, the scene is relatively simple, and the face of VIP is always in frontal view and salient. To tackle these problems, the proposed Unconstrained-7k dataset is featured in two aspects. First, it contains over 7, 000 annotated images, making it the largest VIPLoc dataset under unconstrained conditions to date. Second, our dataset is collected freely on the Internet, including multiple scenes, where images are in unconstrained conditions. VIPs in the new dataset are in different settings, e. g. , large view variation, varying sizes, occluded, and complex scenes. Meanwhile, each image has more persons (> 20), making the dataset more challenging. As a minor contribution, motivated by the observation that VIPs are highly related to not only neighbors but also iconic objects, this paper proposes a Joint Social Relation and Individual Interaction Graph Neural Networks (JSRII-GNN) for VIPLoc. Experiments show that the JSRII-GNN yields competitive accuracy on NCAA (National Collegiate Athletic Association), MS (Multi-scene), and Unconstrained-7k datasets. https: //github. com/xiaowang1516/VIPLoc.

Authors

• Xiao Wang MoE Key Laboratory of Interdisciplinary Research of Computation and Economics, Shanghai University of Finance and Economics
• Zheng Wang National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, China Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, China
• Toshihiko Yamasaki The University of Tokyo
• Wenjun Zeng Eastern Institute of Technology, Ningbo

Keywords

No keywords are indexed for this paper.