2 research outputs found
Hybrid Channel Based Pedestrian Detection
Pedestrian detection has achieved great improvements with the help of
Convolutional Neural Networks (CNNs). CNN can learn high-level features from
input images, but the insufficient spatial resolution of CNN feature channels
(feature maps) may cause a loss of information, which is harmful especially to
small instances. In this paper, we propose a new pedestrian detection
framework, which extends the successful RPN+BF framework to combine handcrafted
features and CNN features. RoI-pooling is used to extract features from both
handcrafted channels (e.g. HOG+LUV, CheckerBoards or RotatedFilters) and CNN
channels. Since handcrafted channels always have higher spatial resolution than
CNN channels, we apply RoI-pooling with larger output resolution to handcrafted
channels to keep more detailed information. Our ablation experiments show that
the developed handcrafted features can reach better detection accuracy than the
CNN features extracted from the VGG-16 net, and a performance gain can be
achieved by combining them. Experimental results on Caltech pedestrian dataset
with the original annotations and the improved annotations demonstrate the
effectiveness of the proposed approach. When using a more advanced RPN in our
framework, our approach can be further improved and get competitive results on
both benchmarks.Comment: 9 pages, 4 figures, Submitted to Neurocomputing, The 5th line of
table 3 was accidentally mistaken. The data have been corrected and the
related descriptions in section 4.4 have also be revised accordingly. Typos
corrected, references correcte
From Handcrafted to Deep Features for Pedestrian Detection: A Survey
Pedestrian detection is an important but challenging problem in computer
vision, especially in human-centric tasks. Over the past decade, significant
improvement has been witnessed with the help of handcrafted features and deep
features. Here we present a comprehensive survey on recent advances in
pedestrian detection. First, we provide a detailed review of single-spectral
pedestrian detection that includes handcrafted features based methods and deep
features based approaches. For handcrafted features based methods, we present
an extensive review of approaches and find that handcrafted features with large
freedom degrees in shape and space have better performance. In the case of deep
features based approaches, we split them into pure CNN based methods and those
employing both handcrafted and CNN based features. We give the statistical
analysis and tendency of these methods, where feature enhanced, part-aware, and
post-processing methods have attracted main attention. In addition to
single-spectral pedestrian detection, we also review multi-spectral pedestrian
detection, which provides more robust features for illumination variance.
Furthermore, we introduce some related datasets and evaluation metrics, and
compare some representative methods. We conclude this survey by emphasizing
open problems that need to be addressed and highlighting various future
directions. Researchers can track an up-to-date list at
https://github.com/JialeCao001/PedSurvey.Comment: 23 pages, 10 figures, Projects:
https://github.com/JialeCao001/PedSurve