With the increasing application of computer vision technology in autonomous driving, robot, and other mobile devices, more and more attention has been paid to the implementation of target detection and tracking algorithms on embedded platforms. The real-time performance and robustness of algorithms are two hot research topics and challenges in this field. In order to solve the problems of poor real-time tracking performance of embedded systems using convolutional neural networks and low robustness of tracking algorithms for complex scenes, this paper proposes a fast and accurate real-time video detection and tracking algorithm suitable for embedded systems. The algorithm combines the object detection model of single-shot multibox detection in deep convolution networks and the kernel correlation filters tracking algorithm, what is more, it accelerates the single-shot multibox detection model using field-programmable gate arrays, which satisfies the real-time performance of the algorithm on the embedded platform. To solve the problem of model contamination after the kernel correlation filters algorithm fails to track in complex scenes, an improvement in the validity detection mechanism of tracking results is proposed that solves the problem of the traditional kernel correlation filters algorithm not being able to robustly track for a long time. In order to solve the problem that the missed rate of the single-shot multibox detection model is high under the conditions of motion blur or illumination variation, a strategy to reduce missed rate is proposed that effectively reduces the missed detection. The experimental results on the embedded platform show that the algorithm can achieve real-time tracking of the object in the video and can automatically reposition the object to continue tracking after the object tracking fails.

随着计算机视觉技术在自动驾驶、机器人等移动设备中的应用越来越广泛，目标检测和跟踪算法在嵌入式平台上的实现越来越受到重视。算法的实时性和鲁棒性是该领域的两个热门研究课题和挑战。针对使用卷积神经网络的嵌入式系统实时跟踪性能差、复杂场景跟踪算法鲁棒性低等问题，提出一种适用于嵌入式系统的快速准确的实时视频检测跟踪算法。该算法结合了深度卷积网络中single-shot multibox检测的目标检测模型和核相关滤波器跟踪算法，更重要的是，它使用现场可编程门阵列加速单次多盒检测模型，满足嵌入式平台上算法的实时性能。针对核相关滤波器算法在复杂场景下无法跟踪后模型污染的问题，提出了对跟踪结果有效性检测机制的改进，解决了传统核相关滤波器算法无法对跟踪结果进行稳健跟踪的问题。很长时间。针对单发多框检测模型在运动模糊或光照变化情况下漏检率较高的问题，提出了一种降低漏检率的策略，有效降低了漏检率。