FPGA-Based Selected Object Tracking Using LBP, HOG and Motion Detection

Tomyslav Sledeviè, A. Serackis, D. Plonis
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引用次数: 9

Abstract

This paper describes the hardware architecture for selected object tracking on an embedded system. The LBP and HOG feature extraction algorithm is combined with motion detection to compute and compare the features vectors with captured once only when the target moves. LBP8,1, LBP16,2, and HOG8,1, HOG16,2 are used to create the feature vector. The unit that makes a final decision on tracker update is based on searching of the least SSD of features' histogram. The implemented motion detection algorithm was able to find and mark eight moving objects simultaneously. The previously computed locations update all trackers' locations in every next frame. The experimental investigation showed that implemented tracker, based on HOG features is robust to luminescence variation and partial occlusion. In addition, the LBP based tracker is robust to the rotation. The proposed architecture is implemented on Xilinx Virtex 4 FPGA using VHDL and is able to work in real-time on 60 fps and $640 \times 480$ video resolution.
基于fpga的基于LBP、HOG和运动检测的选定目标跟踪
本文介绍了嵌入式系统中选定目标跟踪的硬件结构。将LBP和HOG特征提取算法与运动检测相结合,仅在目标运动时计算并比较捕获的特征向量。使用LBP8、1、LBP16、2和HOG8、1、HOG16、2创建特征向量。最终决定跟踪器更新的单位是基于搜索特征直方图的最小SSD。所实现的运动检测算法能够同时发现并标记8个运动物体。之前计算的位置在下一帧更新所有跟踪器的位置。实验结果表明,所实现的基于HOG特征的跟踪器对发光变化和局部遮挡具有较强的鲁棒性。此外,基于LBP的跟踪器对旋转具有鲁棒性。该架构在Xilinx Virtex 4 FPGA上使用VHDL实现,能够以60fps和$640 × 480$的视频分辨率实时工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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