J. Pang, C. Li, J. Shi, Z. Xu, and H. Feng. We organize the first large-scale Tiny Object Detection (TOD) challenge, which is a competition track: tiny person detection. We provide 18433 normal person boxes and 16909 dense boxes in training set. -cnn: Fast tiny object detection in large-scale remote sensing In Figure 1, WIDER Face holds a similar absolute scale distribution to TinyPerson. Only 7 left in stock - order soon. The 1st Tiny Object Detection (TOD) Challenge aims toencourage research in developing novel and accurate methods for tinyobject detection in images which have … annotations will be made publicly and an online benchmark will be setup for algorithm evaluation. A. Ess, B. Leibe, K. Schindler, and L. Van Gool. Xuehui Yu, Yuqi Gong, Nan Jiang, Qixiang Ye, Zhenjun Han; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1) The persons in TinyPerson are quite tiny compared with other representative datasets, shown in Figure 1 and Table 1, which is the main characteristics of TinyPerson; 2) The aspect ratio, of persons in TinyPerson has a large variance, given in Talbe. 圣诞快乐～ 今天分享一篇新出的论文 Scale Match for Tiny Person Detection，作者贡献了一个细小人物目标检测的数据集 TinyPerson，同时提出一种对预训练数据进行尺度调整的Scale Match（尺度匹配）的方法，显著改进了小目标检测。 The tiny relative size results in more false positives and serious imbalance of positive/negative, due to massive and complex backgrounds are introduced in a real scenario. The objects’ relative size of TinyPerson is smaller than that of CityPersons as shown in bottom-right of the Figure 1. Wi, Hi denote the width and height of Ii, respectively. 圣诞快乐～ 今天分享一篇新出的论文 Scale Match for Tiny Person Detection，作者贡献了一个细小人物目标检测的数据集 TinyPerson，同时提出一种对预训练数据进行尺度调整的Scale Match（尺度匹配）的方法，显著改进了小目标检测。 The intuition of our approach is to align the object scales of the dataset for pre- trainingandtheonefordetectortraining. Xuehui Yu, Yuqi Gong, Nan Jiang, Qixiang Ye, Zhenjun Han WACV 2020; HRDNet: High-resolution Detection Network for Small Objects. , we define the probability density function of objects’ size, , which is used to transform the probability distribution of objects’ size in extra dataset. investigated. Our approach is inspired by the Human Cognition Process, while Scale Match can better utilize the existing annotated data and make the detector more sophisticated. Dataset for person detection: Pedestrian detection has always been a hot issue in computer vision. new and we will use the new in latter research. 1257--1265. Scale Match for Tiny Person Detection. 2012 IEEE Conference on Computer Vision and Pattern Scale Match for Tiny Person Detection. And for tiny[2, 20], it is partitioned into 3 sub-intervals: tiny1[2, 8], tiny2[8, 12], tiny3[12, 20]. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. 00. Scale Match for Tiny Person Detection Visual object detection has achieved unprecedented ad-vance with the ris... 12/23/2019 ∙ by Xuehui Yu , et al. For the second step, a uniform sampling algorithm is used. offalse alarms. With detector pre-trained on SM COCO, we obtain 3.22% improvement of APtiny50, Table 7. The transformation of the mean of objects’ size to that in TinyPerson is inefficient. Pattern Recognition. If nothing happens, download GitHub Desktop and try again. [13]. Scale Match for Tiny Person Detection Xuehui Yu, Yuqi Gong, Nan Jiang, Qixiang Ye, Zhenjun Han Visual object detection has achieved unprecedented ad-vance with the rise of deep convolutional neural networks.However, detecting tiny objects (for example tiny per-sons less than 20 pixels) in large-scale images remainsnot well investigated. Several small target datasets including WiderFace [25] and TinyNet [19], have been reported. The extremely small objects raisea grand challenge about feature representation while themassive and complex backgrounds aggregate the … Then, we obtain a new dataset, COCO100, by setting the shorter edge of each image to 100 and keeping the height-width ratio unchanged. Inspired by the Human Cognitive Process that human will be sophisticated with some scale-related tasks when they learn more about the objects with the similar scale, we propose an easy but efficient scale transformation approach for tiny person detection by keeping the scale consistency between the TinyPerson and the extra dataset. With MSM COCO as the pre-trained dataset, the performance further improves to 47.29% of APtiny50, Table 7. rules of AP have updated in benchmark after this paper accepted, So this paper Since some images are with dense objects in TinyPerson, DETECTIONS_PER_IMG (the max number of detector’s output result boxes per image) is set to 200. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie. segmentation. c... Accordingly, we proposea simple yet effective Scale Match approach to align theobject scales between the two datasets for favorable tiny-object representation. It is known that the histogram Equalization and Matching algorithms for image enhancement keep the monotonic changes of pixel values. If nothing happens, download the GitHub extension for Visual Studio and try again. Feature pyramid networks for object detection. Therefore, we use P2, P3, P4, P5, P6 of FPN instead of P3, P4, P5, P6, P7 for RetinaNet, which is similar to Faster RCNN-FPN. The TinyPerson dataset was used for the TOD Challenge and is publicly released. 1257-1265. OverFeat adopted a Conv-Net as a sliding window detector on an image pyramid. [paper] [ECCVW] We follow this idea monotonically change the size, as shown in Figure 6. Scale Match for Tiny Person Detection. Due to only resizing these objects will destroy the image structure. 0 Some annotation examples are given in Figure 2. The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection. A commonly approah is training a model on the extra datasets as pre-trained model, and then fine-tune it on a task-specified dataset. (integer, number of bin in histogram which use to estimate. We build the baseline for tiny person detection and experimentally find that the scale mismatch could deteriorate the feature representation and the detectors. available(https://github.com/ucas-vg/TinyBenchmark). 2009 IEEE conference on computer vision and pattern ∙ 03/20/2020 ∙ by Xuangeng Chu, et al. Person/pedestrian detection is an important topic in the computer vision community. Xuehui Yu, Yuqi Gong, Nan Jiang, Qixiang Ye, Zhenjun Han WACV 2020; Extended Feature Pyramid Network for Small Object Detection. [28] proposed a scale-equitable face detection framework to handle different scales of faces well. Then the absolute size and relative size of a object are calculated as: For the size of objects we mentioned in the following, we use the objects’ absolute size by default. OpenMMLab Detection Toolbox and Benchmark. In Table 4, the MRtiny50 of tiny CityPersons is 40% lower than that of CityPersons. 23 Dec 2019 • Xuehui Yu • Yuqi Gong • Nan Jiang • Qixiang Ye • Zhenjun Han. INPUT: Dtrain (train dataset of D) Firstly, videos with a high resolution are collected from different websites. Many wo... quick maritime rescue and defense around sea, // calculate histogram with uniform size step and have. Best detector: With MS COCO, RetinaNet and FreeAnchor achieves better performance than Faster RCNN-FPN. Chunfang Deng, Mengmeng Wang, Liang Liu, and Yong Liu arXiv 2020; MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection Despite the pedestrians in those datasets are in a relatively high resolution and the size of the pedestrians is large, this situation is not suitable for tiny object detection. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. The publicly available datasets are quite different from TinyPerson in object type and scale distribution, as shown in Figure 1. S. Zhang, R. Benenson, M. Omran, J. Hosang, and B. Schiele. The scale factor incrementally scales the detection resolution between MinSize and MaxSize. Spatial information: Due to the size of the tiny object, spatial information maybe more important than deeper network model. [Paper Reading Note] Scale Match for Tiny Person Detection lovefreedom22 2020-01-29 19:39:06 1345 收藏 4 分类专栏： Detection 文章标签： 行人检测 T.-Y. Scale Match for Tiny Person Detection. This normalization is into float from 0 - 1, The scale parameter normalize all intensity values into the range of 0-1 of blobFromImg in function network.setInput( , , scale, ) parameter. While the region-based methods are complex and time-consuming, single-stage detectors, such as YOLO [20] and SSD [17], are proposed to accelerate the processing speed but with a performance drop, especially in tiny objects. ∙ Rich feature hierarchies for accurate object detection and semantic To better quantify the effect of the tiny relative size, we obtain two new datasets 3*3 tiny CityPersons and 3*3 TinyPerson by directly 3*3 up-sampling tiny CityPersons and TinyPerson, respectively. We annotate 72651 objects with bounding boxes by hand. Visual object detection has achieved unprecedented advance with the rise of deep convolutional neural networks.However, detecting tiny objects (for example tiny persons … 2020. Proceedings of the IEEE Conference on Computer Vision and ∙ 11/26/2020 ∙ by Yanjia Zhu, et al. Mapping object’s size s in dataset E to ^s with a monotone function f, makes the distribution of ^s same as Psize(^s,Dtrain). ∙ detectors. 【文献阅读12】Scale Match for Tiny Person Detection-微小人物检测的尺度匹配 Mr小米周 2020-12-29 12:13:02 50 收藏 分类专栏： 文献阅读 计算机视觉 ∙ mis-match between the dataset for network pre-training and thedataset for Scale Match can transform the distribution of size to task-specified dataset, as shown in Figure 5. In The IEEE Winter Conference on Applications of Computer Vision. February 2, 2020. Scale Match for Tiny Person Detection(WACV2020), Official link of the dataset - ucas-vg/TinyBenchmark Therefore, a more efficient rectified histogram (as show in Algorithm 2) is proposed. For TinyPerson, the RetinaNet[15], FCOS[23], Faster RCNN-FPN, which are the representatives of one stage anchor base detector, anchor free detector and two stage anchor base detector respectively, are selected for experimental comparisons. ∙ They are not applicable to the scenarios where persons are in a large area and in a very long distance, e.g., marine search and rescue on a helicopter platform. Scale Match for Tiny Person Detection Xuehui Yu, Yuqi Gong, Nan Jiang, Qixiang Ye, Zhenjun Han. Proceedings of the IEEE International Conference on Computer And the IOU threshold is set to 0.5 for performance evaluation. If nothing happens, download Xcode and try again. Different from tiny CityPersons, the images in TinyPerson are captured far away in the real scene. we will keep old rules of AP in benchmark, but we recommand the We choose ResNet50 as backbone. Scale Match for Tiny Person Detection Visual object detection has achieved unprecedented ad-vance with the ris... 12/23/2019 ∙ by Xuehui Yu , et al. Object Detectors, The 1st Tiny Object Detection Challenge:Methods and Results, SRN: Side-output Residual Network for Object Symmetry Detection in the We provide 18433 normal person boxes and 16909 dense boxes in training set. However, detector pre-trained on MS COCO improves very limited in TinyPerson, since the object size of MS COCO is quite different from that of TinyPerson. TinyPerson represents the person in a quite low resolution, mainly less than 20 pixles, in maritime and beach scenes. R-CNN adopted a region proposal-based method based on selective search and then used a Conv-Net to classify the scale normalized proposals. networks. TinyNet involves remote sensing target detection in a long distance. We provide 18433 normal person boxes and 16909 dense boxes in training set. ∙ 0 ∙ share It is known that the more data used for training, the better performance will be. J. Deng, W. Dong, R. Socher, L.-J. vision. For Caltech or CityPersons, IOU criteria is adopted for performance evaluation. [14] proposed feature pyramid networks that use the top-down architecture with lateral connections as an elegant multi-scale feature warping method. Use Git or checkout with SVN using the web URL. These image are collected from real-world scenarios based on UAVs. In this paper, we just simply adopt the first way for ignore regions. However in TinyPerson, most of ignore regions are much larger than that of a person. ∙ OUTPUT: H (probability of each bin in the histogram for estimating Psize(s;Dtrain)). Along with the rapid development of CNNs, 3. Training 12 epochs, and base learning rate is set to 0.01, decay 0.1 after 6 epochs and 10 epochs. Scale Match for Tiny Person Detection. TinyPerson. INPUT: K(integer, number of bin in histogram which use to estimate Psize(s;Dtrain)) Scale Match will be applied to all objects in E to get T(E), when there are a large number of targets in E, Psize(s;T(E)) will be close to Psize(s;D). WiderFace mainly focused on face detection, as shown in Figure, In recent years, with the development of Convolutional neural networks (CNNs), the performance of classification, detection and segmentation on some classical datasets, such as ImageNet, , has far exceeded that of traditional machine learning algorithms.Region convolutional neural network (R-CNN), has become the popular detection architecture. Sample ^s: We firstly sample a bin’s index respect to probability of H, and secondly sample ^s respect to a uniform probability distribution with min and max size equal to R[k]− and R[k]+. S3fd: Single shot scale-invariant face detector. The main contributions of our work include: 1. ... Such diversity enables models trained on TinyPerson to well generalize to more scenes, e.g., Long-distance human target detection and then rescue. Fcos: Fully convolutional one-stage object detection. We can ignore the mean, but the scale is important. 0 Therefore, we cut the origin images into some sub-images with overlapping during training and test. Combining Deep Learning and Verification for Precise Object Instance Detection Flood-survivors detection using IR imagery on an autonomous drone. Different from objects in proper scales, the tiny objects are much more challenging due to the extreme small object size and low signal noise ratio, as shown in Figure 1. H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia. Pluto1314/prepare_detection_dataset 0 convert dataset to coco/voc format. The scenarios of existing person/pedestrian benchmarks [2][6][24][5][4][8]， e.g., CityPersons [27], are mainly in a near or middle distance. However, for TinyPerson, the same up-sampling strategy obtains limited performance improvement. The proposed Scale Match approach improves the detection performance over the state-of-the-art detector (FPN) with a significant margin ( 5%). Since the ignore region is always a group of persons (not a single person) or something else which can neither be treated as foreground (positive sample) nor background (negative sample). Chunfang Deng, Mengmeng Wang, Liang Liu, and Yong Liu arXiv 2020; MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection share. For true object detection the above suggested keypoint based approaches work better. NOTE: N (the number of objects in dataset D); Gij(Dtrain) is j-th object in i-th image of dataset Dtrain. Freeanchor: Learning to match anchors for visual object detection. 23 Dec 2019 • ucas-vg/TinyBenchmark. 4.4 out of 5 stars 102. distance and with mas-sive backgrounds. How can we use extra public datasets with lots of data to help training model for specified tasks, e.g., tiny person detection? The performance drops significantly while the object’s size becomes tiny. In this paper, without losing generality, MS COCO is used as extra dataset, and Scale Match is used for the scale transformation T. Gij=(xij,yij,wij,hij) represents j-th object in image Ii of dataset E. The Scale Match approach can be simply described as three steps: Resize object with scale ratio c ,then ^Gij←(xij∗c,yij∗c,wij∗c,hij∗c); where ^Gij is the result after Scale Match. ∙ Citypersons: A diverse dataset for pedestrian detection. 09/16/2020 ∙ by Xuehui Yu, et al. 2008 IEEE Conference on Computer Vision and Pattern ∙ Advances in neural information processing systems. ok，今天分享的就是小目标检测方向的最新论文：Scale Match for Tiny Person Detection。这篇论文的"模式"也是一种较为经典的方式:新数据集+新benchmark，也就是提出了新的小目标检测数据集和小目标检测方法。 Scale Match for Tiny Person Detection [challenge] 03/07/2017 ∙ by Wei Ke, et al. It’s hard to have high location precision in TinyPerson due to the tiny objects’ absolute and relative size. Recognition. But it obtained poor performance on TinyPerson, due to the great difference between relative scale and aspect ratio, which also further demonstrates the great chanllange of the proposed TinyPerson.
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