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Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection [article]

Chengyang Li, Dan Song, Ruofeng Tong, Min Tang
2018 arXiv   pre-print
With this in mind, we propose an Illumination-aware Faster R-CNN (IAF RCNN). Specifically, an Illumination-aware Network is introduced to give an illumination measure of the input image.  ...  The experimental results on KAIST Multispectral Pedestrian Benchmark validate the effectiveness of the proposed IAF R-CNN.  ...  Acknowledgements The research is supported in part by NSFC (61572424) and the Science and Technology Department of Zhejiang Province (2018C01080).  ... 
arXiv:1803.05347v2 fatcat:vmaeoqky5ncsbcngbdfeyrfu2m

Faster R-CNN for Robust Pedestrian Detection Using Semantic Segmentation Network

Tianrui Liu, Tania Stathaki
2018 Frontiers in Neurorobotics  
Our method extends the Faster R-CNN detection framework by adding a branch of network for semantic image segmentation.  ...  We make use of multi-resolution feature maps extracted from different network layers in order to ensure good detection accuracy for pedestrians at different scales.  ...  We adopt the anchor mechanism of Faster R-CNN (Ren et al., 2015) to enable simultaneously addressing of multiple scales detection on a single scale testing image.  ... 
doi:10.3389/fnbot.2018.00064 pmid:30344486 pmcid:PMC6182048 fatcat:ixv426fc3zdobk3npndx4jlpfq

Face R-CNN [article]

Hao Wang, Zhifeng Li, Xing Ji, Yitong Wang
2017 arXiv   pre-print
In our approach, we exploit several new techniques including new multi-task loss function design, online hard example mining, and multi-scale training strategy to improve Faster R-CNN in multiple aspects  ...  In this report, we propose a robust deep face detection approach based on Faster R-CNN.  ...  Multi-Scale Training Instead of using a fixed scale for all the training images in the typical Faster R-CNN framework, we design a multi-scale representation for each image by resizing the original image  ... 
arXiv:1706.01061v1 fatcat:3rgtysbtw5edxdwqks6auawjui

Building Facade Parsing R-CNN [article]

Sijie Wang, Qiyu Kang, Rui She, Wee Peng Tay, Diego Navarro Navarro, Andreas Hartmannsgruber
2022 arXiv   pre-print
We propose Facade R-CNN, which includes a transconv module, generalized bounding box detection, and convex regularization, to perform parsing of deformed facade views.  ...  Experiments demonstrate that Facade R-CNN achieves better performance than the current state-of-the-art facade parsing models, which are primarily developed for frontal views.  ...  Using a multi-scaled CNN backbone [He et al., 2016] is robust to scale changes.  ... 
arXiv:2205.05912v1 fatcat:bbnajnggozd4xp2slttmlahpue

Scale-aware Fast R-CNN for Pedestrian Detection [article]

Jianan Li, Xiaodan Liang, ShengMei Shen, Tingfa Xu, Jiashi Feng, Shuicheng Yan
2016 arXiv   pre-print
Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework.  ...  Outputs from all the sub-networks are then adaptively combined to generate the final detection results that are shown to be robust to large variance in instance scales, via a gate function defined over  ...  SAF R-CNN is only a little slower than "Fast R-CNN singlescale" and Faster R-CNN, and 9.0× faster than R-CNN and 5.2× faster than "Fast R-CNN multi-scale".  ... 
arXiv:1510.08160v3 fatcat:elnhnv22tnajxeiunmbrftql7q

MS-Faster R-CNN: Multi-Stream Backbone for Improved Faster R-CNN Object Detection and Aerial Tracking from UAV Images

Danilo Avola, Luigi Cinque, Anxhelo Diko, Alessio Fagioli, Gian Luca Foresti, Alessio Mecca, Daniele Pannone, Claudio Piciarelli
2021 Remote Sensing  
The proposed architecture is then used as backbone for the well-known Faster-R-CNN pipeline, defining a MS-Faster R-CNN object detector that consistently detects objects in video sequences.  ...  Tracking objects across multiple video frames is a challenging task due to several difficult issues such as occlusions, background clutter, lighting as well as object and camera view-point variations,  ...  Acknowledgments: This work was partially supported by both the ONRG project N62909-20-1-2075 "Target Re-Association for Autonomous Agents" (TRAAA) and MIUR under grant "Departments of Excellence 2018-2022  ... 
doi:10.3390/rs13091670 doaj:ab78694468e540bd9bace0bb6c9b5484 fatcat:sgr2bggbj5c5noa3g2suevvuri

Multi target pigs tracking loss correction algorithm based on Faster R-CNN

Longqing Sun, Yuanbing Zou, Yan Li, Zhengda Cai, Yue Li, Bing Luo, Yan Liu, Yiyang Li
2018 International Journal of Agricultural and Biological Engineering  
The video of live pigs was processed by Faster R-CNN to get the object bounding box.  ...  R-CNN.  ...  Acknowledgments The authors acknowledge that this research was financially supported by the National High Technology Research and Development Program of China (2013AA102306).  ... 
doi:10.25165/j.ijabe.20181105.4232 fatcat:qeuygvln5rfgvgo5ejvl5j4lwm

Multi-Person Tracking Based on Faster R-CNN and Deep Appearance Features [chapter]

Gulraiz Khan, Zeeshan Tariq, Muhammad Usman Ghani Khan
2019 Visual Object Tracking in the Deep Neural Networks Era [Working Title]  
Object detection accuracy has been increased by employing deep learning-based Faster region convolutional neural network (Faster R-CNN) algorithm.  ...  Mostly computer vision problems related to crowd analytics are highly dependent upon multi-object tracking (MOT) systems.  ...  Overall performance and accuracy of Faster R-CNN is better than all the traditional object detectors. A diagram for Faster R-CNN is given in Figure 6 .  ... 
doi:10.5772/intechopen.85215 fatcat:oc3kfowngfei5gmeh33uu4dwhy

Mask Scoring R-CNN [article]

Zhaojin Huang, Lichao Huang, Yongchao Gong, Chang Huang, Xinggang Wang
2019 arXiv   pre-print
In this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks.  ...  By extensive evaluations on the COCO dataset, Mask Scoring R-CNN brings consistent and noticeable gain with different models, and outperforms the state-of-the-art Mask R-CNN.  ...  Table 2 indicates that our MS R-CNN is robust to different framework including Faster R-CNN/FPN/DCN+FPN.  ... 
arXiv:1903.00241v1 fatcat:hl4wjgod4fctpbc7w2p74zloee

MOTS R-CNN: Cosine-margin-triplet loss for multi-object tracking [article]

Amit Satish Unde, Renu M. Rameshan
2021 arXiv   pre-print
We propose a scale-invariant tracking by using a multi-layer feature aggregation scheme to make the model robust against object scale variations and occlusions.  ...  We show that the MOTS R-CNN reduces the identity switching by 62% and 61% on cars and pedestrians, respectively in comparison to Track R-CNN.  ...  We show the robustness of our model to occlusions and scale variation which can be attributed to the multi-layer feature aggregation mechanism.  ... 
arXiv:2102.03512v1 fatcat:36kcjvud5rhxpfxnxyylzyzivy

Double Anchor R-CNN for Human Detection in a Crowd [article]

Kevin Zhang, Feng Xiong, Peize Sun, Li Hu, Boxun Li, Gang Yu
2019 arXiv   pre-print
The proposed framework, called Double Anchor R-CNN, is able to detect the body and head for each person simultaneously in crowded scenarios.  ...  In this paper, we propose to handle the crowd occlusion problem in human detection by leveraging the head part. Double Anchor RPN is developed to capture body and head parts in pairs.  ...  Our approach is also extensive and can be easily generalized to detect other parts, for example, detecting the head, face and body of each person with triple anchor R-CNN.  ... 
arXiv:1909.09998v1 fatcat:7mr4uvgo5vc43b5tvy7ucskuqi

Multi-region Two-Stream R-CNN for Action Detection [chapter]

Xiaojiang Peng, Cordelia Schmid
2016 Lecture Notes in Computer Science  
We propose a multi-region two-stream R-CNN model for action detection in realistic videos.  ...  in the faster R-CNN model, which adds complementary information on body parts.  ...  This work was supported in part by the ERC advanced grant ALLEGRO, the MSR-Inria joint project, a Google research award, a Facebook gift, the Natural Science Foundation of China (No. 61502152) and the  ... 
doi:10.1007/978-3-319-46493-0_45 fatcat:762aicweybhlje62elalajtkrq

Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion [article]

Wenbo Dong, Pravakar Roy, Cheng Peng, Volkan Isler
2020 arXiv   pre-print
We first propose a robust and compact ellipse regression based on the Mask R-CNN architecture for elliptical object detection.  ...  We introduce the first CNN-based ellipse detector, called Ellipse R-CNN, to represent and infer occluded objects as ellipses.  ...  Similar to Faster R-CNN [6] , Mask R-CNN-bbox outputs bounding-box detections. This baseline model is directly modified from Mask R-CNN by removing the segmentation branch.  ... 
arXiv:2001.11584v2 fatcat:x5g3mpdux5denclk67di6hnjzq

An Improved Faster R-CNN Pedestrian Detection Algorithm Based on Feature Fusion and Context Analysis

Sheping Zhai, Susu Dong, Dingrong Shang, Shuhuan Wang
2020 IEEE Access  
FCF R-CNN Compared with Faster R-CNN, the improvement of our method is to design a multi-scale feature extraction network and a multi-layer LSTM module for global context extraction.  ...  pedestrian dataset showing competitive speed, accuracy and robustness in challenging scene such as multi-scale and occlusion.  ... 
doi:10.1109/access.2020.3012558 fatcat:xzwv3wjfdvawncjlo3jm5fujqu

Exploring the Applications of Faster R-CNN and Single-Shot Multi-box Detection in a Smart Nursery Domain [article]

Somnuk Phon-Amnuaisuk, Ken T. Murata, Praphan Pavarangkoon, Kazunori Yamamoto, Takamichi Mizuhara
2018 arXiv   pre-print
In this paper, the Faster Region-based Convolutional Neural Network and the Single-Shot Multi-Box Detection approaches are explored.  ...  Recent advances in deep learning and computer vision offer various powerful tools in general object detection and can be applied to a baby detection task.  ...  Acknowledgments We would like to thank the Centre for Innovative Engineering (Universiti Teknologi Brunei), NICT (Japan) and CLEALINKTECHNOLOGY (Japan) for their supports given to this work.  ... 
arXiv:1808.08675v1 fatcat:ibtqq2njuvc3fm4sfrmej3ptvy
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