A Novel Wide-Area Multiobject Detection System with High-Probability Region Searching
Xianlei Long, Hui Zhao, Chao Chen, Fuqiang Gu, Qingyi Gu

TL;DR
This paper introduces a hybrid wide-area detection system combining panoramic imaging and high-speed search to efficiently locate multiple objects in large areas, achieving high frame rates and improved accuracy.
Contribution
The paper presents a novel method using panoramic probability maps and dynamic search modules for fast, accurate multi-object detection in wide-area surveillance.
Findings
Achieves 120 fps multi-object detection.
Effectively estimates high-probability regions for objects.
Demonstrates robustness in extensive experiments.
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)…
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Taxonomy
TopicsAdvanced Algorithms and Applications
