In recent years, wide-area visual surveillance systems have been widely applied in various industrial and transportation scenarios. These systems, however, face significant challenges when implementing multi-object detection due to conflicts arising from the need for high-resolution imaging, efficient object searching, and accurate localization. To address these challenges, this paper presents a hybrid system that incorporates a wide-angle camera, a high-speed search camera, and a galvano-mirror. In this system, the wide-angle camera offers panoramic images as prior information, which helps the search camera capture detailed images of the targeted objects. This integrated approach enhances the overall efficiency and effectiveness of wide-area visual detection systems. Specifically, in this study, we introduce a wide-angle camera-based method to generate a panoramic probability map (PPM) for estimating high-probability regions of target object presence. Then, we propose a probability searching module that uses the PPM-generated prior information to dynamically adjust the sampling range and refine target coordinates based on uncertainty variance computed by the object detector. Finally, the integration of PPM and the probability searching module yields an efficient hybrid vision system capable of achieving 120 fps multi-object search and detection. Extensive experiments are conducted to verify the system's effectiveness and robustness.

翻译：近年来，广域视觉监控系统已广泛应用于各类工业与交通场景。然而，此类系统在多目标检测中面临显著挑战，其原因在于高分辨率成像、高效目标搜索与精确定位之间存在需求冲突。为解决上述问题，本文提出一种融合广角相机、高速搜索相机与振镜的混合系统。在该系统中，广角相机提供全景图像作为先验信息，辅助搜索相机捕获目标物体的细节图像。该集成方法有效提升了广域视觉检测系统的整体效率与效果。具体而言，本研究引入基于广角相机的方法生成全景概率图（PPM），用于估计目标物体存在的高概率区域。继而提出一种概率搜索模块，该模块利用PPM生成的先验信息动态调整采样范围，并基于目标检测器计算的不确定性方差精化目标坐标。最终，将PPM与概率搜索模块整合，构建出一种高效的混合视觉系统，能够实现120帧/秒的多目标搜索与检测。通过大量实验验证了该系统的有效性与鲁棒性。