  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.
