Pedestrian detection in digital videos using committee of motion feature extractors

Diogo L. da Silva, L. Seijas, C. J. A. B. Filho
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Abstract

Human detection in digital images is a challenge because the motion of the camera, background and variations in pose, appearance, clothing and illumination introduce difficulties for person detection. Several pedestrian detectors were proposed recently, such as the Aggregated Channel Features (ACF). These types of detectors are based on features related to the shape of the object. These detectors generate many false alarms. In this paper we propose the use of motion features in the ACF framework to mitigate false alarms emitted. Three motion features are proposed: WSTD, MBH and IMHcd. We combined these features with ACF. Then, committees of classifiers were created from these feature combinations improving original ACF results and reducing false positives per image (FPPI). An improvement on the miss rate for 100 FPPI and on the log-average miss rate was obtained, reducing these values in 19 and 8.46 percentage points respectively on Caltech dataset.
基于运动特征提取器的数字视频行人检测
数字图像中的人体检测是一项挑战,因为相机的运动、背景和姿势、外观、服装和照明的变化给人体检测带来了困难。近年来提出了几种行人检测器,如聚合通道特征(ACF)。这些类型的探测器是基于与物体形状相关的特征。这些检测器产生许多假警报。在本文中,我们提出在ACF框架中使用运动特征来减少发出的假警报。提出了三种运动特征:WSTD、MBH和IMHcd。我们将这些特性与ACF结合起来。然后,从这些特征组合中创建分类器委员会,改进原始ACF结果并减少每幅图像的误报(FPPI)。在加州理工学院数据集上,100 FPPI的失分率和对数平均失分率分别降低了19和8.46个百分点。
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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