Abstract
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.
Abstract (translated)
近年来，随着各种工业和交通场景中大面积视觉监视系统的广泛应用，这些系统在实施多目标检测时遇到了显著的挑战。然而，由于需要高分辨率成像、高效目标搜索和准确的位置跟踪等冲突，这些系统在实施多目标检测时遇到了困难。为解决这些问题，本文提出了一种集成式系统，该系统包括一个 wide-angle 相机、一个高速搜索相机和一个 galvano-mirror。在这个系统中，广角相机提供全景图像作为先验信息，帮助搜索相机捕捉目标对象的详细图像。这种集成方法提高了大面积视觉检测系统的整体效率和效果。 具体来说，在本研究中，我们提出了一种基于广角相机的自适应方法，用于生成目标物体存在高概率区域的全景概率图（PPM）。然后，我们提出了一种基于PPM生成的先验信息的概率搜索模块，根据物体检测器计算的不确定性方差动态调整采样范围并优化目标坐标。最后，PPM 和概率搜索模块的集成产生了一种能够实现120 fps 多目标搜索和检测的高效混合视觉系统。 为了验证系统的有效性和稳健性，进行了大量实验。
URL
https://arxiv.org/abs/2405.04589
