Pedestrian detection using first- and second-order aggregate channel features
Tóm tắt
The content-based analysis of visual multimedia like images and videos are urgently needed to empower human society for the automation of difficult tasks. Pedestrian detection serves as a backbone for a multitude of image processing and machine learning algorithms and secures quite a lot of real-world applications. Keeping this fact in mind, here, we deal with the fabrication of suitable features to identify human/pedestrian instances from images with near accuracy. Accordingly, we introduce second-order aggregate channel features (SOACF) to enhance the performance of much-celebrated pedestrian detection algorithm which was mainly based on the first-order information in an image—aggregate channel features detector (ACF detector). We experimentally proved the complementary nature of ACF and SOACF. Designed to garner both these features together, instead of simple concatenation, or direct merging of the two detectors, we employed a weighted non-maximum suppression merging algorithm. The prospective detector not only performed well on INRIA, Caltech and KITTI pedestrian data set but also, mitigate the miss rate by
$$\sim 4\%$$
in Caltech data set and
$$\sim 2\%$$
in KITTI data set in comparison with ACF detector. Despite the fact that our in-house generated detector uses only a few channels, it surpasses many state-of-the-art methods based on baseline ACF detector. Moreover, the detection speed is 100 times faster than the topmost pedestrian detector based on ACF.
Tài liệu tham khảo
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