ABSTRACT Comprehensive observation of target area with pedestrians through pan‐tilt‐zoom (PTZ) camera networks is crucial in various surveillance applications. However, the dynamic configuration of PTZ cameras increases the difficulty of coordinating multiple cameras to monitor large‐scale scenes. Since coverage control in PTZ camera networks has been proven to be an NP‐hard problem, many studies have adopted virtual potential field (VPF) algorithms to efficiently obtain approximate solutions. The VPF methods treat camera viewpoints as charged particles. Through repulsive forces between these particles, PTZ camera networks expand scene coverage and reduce overlap between camera fields of view (FoVs). However, VPF‐based methods cannot leverage the scene layout and target priority information, failing to cover pedestrians and other critical areas. In this work, we introduce a unified perception quality measure framework that quantifies surveillance importance for scenes, cameras, and pedestrians. Building on this framework, we design a coverage control scheme using a perceptive quality‐based virtual potential field. This scheme models target regions and pedestrian priorities as virtual gravitational and attractive forces. It maximizes coverage of key regions, minimizes camera overlap, and supports high‐resolution monitoring and tracking of pedestrians. Extensive experiments show that our approach outperforms state‐of‐the‐art methods, achieving superior scene and pedestrian coverage performance.

Scene Coordinate Regression (SCR) estimates 3D scene coordinates from 2D images, and has become an important approach in visual relocalization. Existing methods exhibit high localization accuracy in small scenes, but still face substantial challenges in large-scale scenes, which usually have significant variations in depth, scale, and occlusion. Although structure-guided scene partitioning is commonly adopted, the over-partitioned elements and large feature variances within subscenes impede the estimation of the 3D coordinates, introducing misleading information for subsequent processing. To address the above-mentioned issues, we propose the Semantic-Structural Determined Visual Relocalization method for SCR, which leverages semantic-structural partition learning and partition-determined pose refinement to better understand the semantic and structural information on large scenes. Firstly, we partition the scene into small subscenes with label assignments, ensuring semantic consistency and structural continuity within each subscene. A classifier is then trained with sampling-based learning to predict these labels. Secondly, the partition predictions are encoded into embeddings and integrated with local features for intra-class compactness and inter-class separation, producing partition-aware features. To further decrease feature variances, we employ a discriminability metric and suppress ambiguous points, improving subsequent computations. Experimental results on the Cambridge Landmarks dataset demonstrate that the proposed method achieves significant improvements with fewer training costs on large-scale scenes, reducing the median error by 38% compared to the state-of-the-art SCR method DSAC*. Code is available: https://gitee.com/VR_NAVE/ss-dvr.

ABSTRACT Pan‐tilt‐zoom (PTZ) cameras, which dynamically adjust their field of view (FOV), are pervasive in large‐scale scenes, such as train stations, squares, and airports. In real scenarios, PTZ cameras are required to quickly judge their directions using contextual clues from the surrounding environment. To achieve this goal, some research projects camera videos into three‐dimensional (3D) models or panoramas and allows operators to establish spatial relationships. However, these works face several challenges in terms of real‐time processing, localization accuracy, and realistic reference. To address this problem, a visual expansion and real‐time calibration for PTZ cameras assisted by panoramic models is proposed. The calibration method consists of three parts: Providing a real environment background by building a panoramic model, meeting the needs of real‐time processing by establishing a PTZ camera motion estimation model and achieving high‐precision alignment between PTZ images and panoramic models using only two feature point pairs. Our methods were validated using both the public and our Scene dataset. The experimental results indicate that our method outperforms other state‐of‐the‐art methods in terms of real‐time processing, accuracy, and robustness.

Pan-Tilt-Zoom (PTZ) camera networks are pervasive in many applications, such as video surveillance, sports analysis and epidemic prevention. However, it is difficult to control several PTZ cameras observing a large scene due to the high complexity of PTZ camera networks. This problem, called the coverage control for PTZ cameras, draws broad attention of researchers. Virtual potential field (VPF) can enhance the coverage performance and reduce the overlap region of cameras, but it does not fully take the actual scenario into account. In this work, we introduce scene potential map (SPM) to the VPF method, investigating both the scene and the task in the optimization. The proposed scene potential map characterizes the importance of target region and evaluates the perception quality of PTZ camera. Then we propose a novel virtual force analysis method to optimalize poses of the PTZ camera network. We also develop a region partition method based on the perception quality measure to divide the target region and achieve better zoom level, realizing an excellent coverage performance of PTZ camera networks. Finally, the evaluation experiments clearly demonstrated that our proposed coverage control scheme can achieve remarkably better performance than state-of-the-arts.