Knowledge Commons of Institute of Automation，CAS
（3）针对广域目标探测任务中，高速目标检测算法定位精度差的问题，设计了一种多帧信息融合的硬件高速目标检测方法，实现了10,000 帧/秒的高速目标定位。为解决已有的基于HOG 特征的高速硬件目标检测算法因检测窗口步长过大导致定位误差大的问题，提出多帧图像目标信息融合技术。首先，制定了检测区域自适应调整策略，有效减少原始检测窗特征提取的硬件资源消耗。其次，提出基于多帧信息同步的数据融合生成方法，显著降低了多帧信息融合的目标位置误差。最终，基于FPGA 硬件平台实现了本文提出的高速目标检测算法，该系统能够同时获得目标的位置与图像数据。实验结果表明，所设计的方法能够在保持高速目标检测性能的同时有效降低定位误差。
In recent years, with the rapid development of computer vision algorithms, object detection-based wide-area video surveillance technology has made tremendous success in the fields of intelligent transportation, public security, sports broadcasting, military situational awareness, and more. However, when dealing with real-world environments like complex scenes, large monitoring Field-of-Views (FOVs), and dynamically distributed objects, the performance of traditional surveillance technology has major deficiencies in terms of searching efficiency, detection speed, imaging quality, covering FOV, etc. How to achieve fast and accurate object searching and detection abilities while capturing high-quality images of objects of interest requires further in-depth study. Consequently, aiming at the key issues in wide-area multiobject detection, this dissertation starts from the aspects of perceptual FOV expansion, wide-area scene modeling, and fast object detection, which conducts several studies in a coarse-grained to fine-grained manner. The main work and contributions of this dissertation are as follows.
(1) A mechanical particle filter-based active vision system for wide-area multiobject detection is proposed to deal with object detection problems in a large scene, which achieved 300 fps detection speed. In the scenario of a wide area, the low target image resolution and irregular distribution of objects of interest make existing detection algorithms poor in search efficiency and lower localization accuracy. To simultaneously achieve a large covering FOV and high-speed detection performance, a large FOV multiobject detection algorithm is proposed. Firstly, an optical scanning galvano-mirror and a high-speed vision platform are utilized to provide fast FOV switching, which achieves a wide-area covering ability. Secondly, to improve the detection efficiency, a mechanical particle filter framework is proposed to construct the objects' probability distribution model and iteratively locate the target object's position in the large FOV, where a single particle is represented by an image that is captured by the galvano-mirror. Finally, a center-partitioned scanning path planning algorithm is introduced to reduce the total solving and scanning costs, where all the particles are partitioned into several particle sets according to their weights and distances to the center particles. The experimental results show that the proposed methods could detect desired objects distributed in a wide area quickly and accurately.
(2) A region probability map-guided fast wide-area multiobject detection method is proposed for the problem of low object searching efficiency in a wide area. Since many wide-area detection methods are modeling the distribution of the whole large scene, resulting in many sampling images containing invalid information during the early stages of searching, they cannot provide effective guidance for the next stage. To improve searching efficiency, this dissertation first proposes a region probability map generation algorithm to process the panoramic image. This algorithm employs a semantic segmentation model to segment the panoramic image that corresponds to the wide-area scene. By considering the prior statistical information of object-region semantic relationships and the scene structure, a region probability measurement function is built. Subsequently, when applying this function to the segmentation mask, each region is assigned a probability measurement that indicates how likely the desired object will occur in that region. Finally, a region probability map-guided particle filter method is proposed to proportionally assign different numbers of particles to different regions according to the relative probabilities, which leads to the sampling particles being concentrated more on high-probability regions at the early stage. The results of the evaluation and generalization experiments show that the proposed detection method could efficiently model the probability of the whole scenario while achieving superior real-time performance, higher accuracy, and fast detection speed. The method can improve the target detection efficiency by a maximum of 38.38%.
(3) To compensate for the high-speed object detection deviation, an FPGA-based ultrahigh-speed object detection method with multi-frame information fusion is proposed, which achieves 10,000 fps target localization. Owing to the localization deviation caused by the large stride between detection windows, the Histogram of Oriented Gradient (HOG) feature-based high-speed detection algorithm is limited in localization accuracy. This dissertation introduces an image data and synchronization signals module. Firstly, a starting point of detection area adjustable strategy is designed to reduce the fixed stride in the original detection window. Subsequently, to accurately represent the start point of the detection window, a synchronization signals module is proposed for real-time detection area synchronization. Based on these proposed modules, once the detector detects objects of interest, the post-process module could simultaneously get the position of starting point and the image data. Then, the algorithm only stores the maximum HOG feature and its corresponding coordinates at the bottom of the image frame. Eventually, the proposed method is implemented on the FPGA-based high-speed vision platform. Experiments show that the designed algorithm could achieve ultrahigh speed while reducing localization deviations.
(4) Aiming for the problem of slow perception of wide-area FOV, a real-time wide-area multiobject detection system is constructed by utilizing a 2-axis galvano-mirror for high-speed FOV expansion. Compared with simulation and indoor environments, the outdoor scenarios are full of object movements and environmental noises. To verify the effectiveness and robustness of the above proposed wide-area multiobject detection algorithms, an active detection system that is based on a panoramic camera, a high-speed telephoto camera, and an ultrafast switching galvano-mirror is implemented. Specifically, the principle of rapid FOV expansion for galvano-mirror is analyzed, and the information processing relationship between each module of the detection system is briefly described. Then, to improve the system's real-time performance, a visual feedback-based high-parallelism control method is proposed. The method integrates the region probability map generation module and the particle filter detection method of the wide-area detection algorithm into a high-parallelism, low-latency control paradigm, resulting in a detection acceleration. Eventually, by using the constructed wide-area multiobject detection system, extensive experiments are conducted on the complex environment to verify the effectiveness and robustness of the system.
|Keyword||高速视觉 目标检测 机械粒子滤波 视觉反馈 广域监控|
|IS Representative Paper||是|
|Sub direction classification||智能硬件|
|planning direction of the national heavy laboratory||环境多维感知|
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