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1st IEEE International Smart Cities Conference, ISC2 2015

作者： Qianyin, Jiang Guoming, Li Jinwei, Yu Xiying, Li Research Center of Intelligent Transportation System School of Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Transportation System Guangzhou China Key Laboratory of Video and Image Intelligent Analysis and Application Technology of MPS Guangzhou China

ISBN: (纸本)9781467365529

In order to reduce traffic accidents caused by the pedestrian, five kinds of dangerous pedestrian abnormal behaviors are studied in the paper. A behavior model between the pedestrian trajectory and the road is built to describe the five kinds of dangerous pedestrian abnormal behaviors: crossing road border, illegal stay, crossing the road, moving along the curb, entering road area. The method contains pedestrian detection, shadow elimination, pedestrian recognition, pedestrian tracking and abnormal behavior detection. Background subtraction method is used to detect moving targets. After shadow elimination, pedestrians are distinguished from vehicles according to the ratio. Then, pedestrian trajectories are gotten by pedestrian tracking. Finally, based on the relation between trajectory and road, the model of five kinds of pedestrian abnormal behaviors is established, and abnormal behaviors are detected according this model. Experiments show that the method can distinguish and detect the pedestrian abnormal behaviors effectively in short time, and it is suitable to use in real time traffic monitoring. © 2015 IEEE.

关键词： Trajectories

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Shadow detection of moving objects in traffic monitoring video 9

Shadow detection of moving objects in traffic monitoring vid...

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9th IEEE Joint International Information technology and Artificial Intelligence Conference, ITAIC 2020

作者： Zhang, Mingrui Zhao, Wenbing Li, Xiying Wang, Dan Beijing University of Technology College of Computer Science and Technology Beijing China Sun Yat-sen University Intelligent Transportation Research Center School of Intelligent System Engineering Guangzhou China Key Laboratory of Intelligent Transportation System of Guangdong Province Guangzhou China Key Laboratory of Video Image Intelligent Analysis and Application Technology Ministry of Public Security Guangzhou China

Moving object detection is an important application of computer vision. Commonly used foreground separation algorithms such as Gaussian mixture modeling, ViBe, frame difference method, etc., do not consider the color of shadow and recognize the shadow of a moving object as a part of the moving object. In many cases, the shadow detection effect is not good. Focus on the detection of moving object shadows in traffic surveillance videos, this paper improves the existing ViBe algorithm, considers the color characteristics of the shadows, recognizes the shadows as part of the background, gains a smaller amount of calculation and better effect of shadow detection with the advantages of ViBe. © 2020 IEEE.

关键词： Security systems

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A Car Face Parts Detection Algorithm Based on Faster R-CNN 18

A Car Face Parts Detection Algorithm Based on Faster R-CNN

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18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018

作者： Zhou, Zhihao Li, Xiying Qiu, Mingkai Research Center of Intelligent Transportation System School of Engineering Sun Yat-sen Univ. Guangzhou Guangdong510006 China Guangdong Provincial Key Laboratory of Intelligent Transportation System Guangzhou Guangdong510006 China Key Laboratory of Video and Image Intelligent Analysis and Application Technology Ministry of Public Security Guangzhou Guangdong510006 China

ISBN: (数字)9780784481523

ISBN: (纸本)9780784481523

The main appearance difference between different types of vehicles is located in the front face area, so the car face parts detection is a key role in fine-grained vehicle recognition. This paper presents a faster R-CNN-based method to detect the position and identify each part of vehicle front face in a complex environment. First, the object information is carried out by K-means clustering and the feature is extracted by VGG-16 network. Second, the candidate regions are obtained by region proposal network (RPN), then uses the Fast R-CNN to obtain the categories and location information of vehicle front face parts. In this paper, 4199 vehicle images of CompCars dataset were used for network training and testing. The experimental results show that the average IoU of the vehicle front face parts is 74.97% and the average recognition precision is 89.59%. Compared with other object detection algorithms, the proposed algorithm shows excellent performance in detecting ability and recognition effect. © 2018 American Society of Civil Engineers.

关键词： Vehicles

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Shadow Detection Of Moving Objects In Traffic Monitoring video

Shadow Detection Of Moving Objects In Traffic Monitoring Vid...

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IEEE Joint International Information technology and Artificial Intelligence Conference (ITAIC)

作者： Mingrui Zhang Wenbing Zhao Xiying Li Dan Wang College of Computer Science and Technology Beijing University of Technology Beijing China Intelligent Transportation Research Center School of Intelligent System Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Intelligent Transportation System of Guangdong Province Guangzhou China Key Laboratory of Video Image Intelligent Analysis and Application Technology Ministry of Public Security Guangzhou China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

关键词： Surveillance Conferences Color Object detection Traffic control Information technology videos

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Weakly supervised histopathology tissue semantic segmentation with multi-scale voting and online noise suppression

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Engineering applications of Artificial Intelligence 2025年 156卷

作者： Xipeng Pan Hualong Zhang Huahu Deng Huadeng Wang Lingqiao Li Zhenbing Liu Lin Wang Yajun An Cheng Lu Zaiyi Liu Chu Han Rushi Lan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi 541004 China Department of Nasopharyngeal Carcinoma Sun Yat-sen University Cancer Center Guangzhou 510060 China Department of Radiology Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou 510080 Guangdong China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangzhou 510080 Guangdong China International Joint Research Laboratory of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin 541004 Guangxi China

The development of an Artificial Intelligence (AI) assisted tissue segmentation method of digital pathology images is critical for cancer diagnosis and prognosis. Excellent performance has been achieved with the current fully supervised segmentation approach, which relies on a huge number of annotated data. However, drawing dense pixel-level annotations on the giga-pixel whole slide image (WSI) is extremely time-consuming and labor-intensive. To this end, we propose a tissue segmentation method using only patch-level classification labels to reduce such annotation burden and significantly improve the quality of the pseudo-masks. We introduce a framework with two phases of classification and segmentation. In the classification phase, we propose a multi-scale voting method on the Class Activation Map (CAM) based model to obtain more stable pseudo masks. In the segmentation phase, an Online Noise Suppression Strategy (ONSS) is proposed to encourage the model to focus on more reliable signals in the pseudo mask rather than noisy signals. Extensive experiments on two weakly supervised pathology image tissue segmentation datasets Lung Adenocarcinoma (LUAD-HistoSeg) and Breast Cancer Semantic Segmentation (BCSS-WSSS) demonstrate our model outperforms state-of-the-art weakly-supervised semantic segmentation (WSSS) methods using patch-level labels. Furthermore, our method exhibits superior generalization ability compared to other models, and demonstrates promising adaptation performance on unseen domains with only small amounts of data.

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More than Encoder: Introducing Transformer Decoder to Upsample

More than Encoder: Introducing Transformer Decoder to Upsamp...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Yijiang Cai, Wentian Gao, Ying Li, Chengming Hu, Xiping South China University of Technology School of Computer Science and Engineering Guangzhou China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen China Beijing Institute of Technology School of Medical Technology Beijing China

ISBN: (纸本)9781665468190

Medical image segmentation methods downsample images for feature extraction and then upsample them to restore resolution for pixel-level predictions. In such schema, upsample technique is vital in restoring information for better performance. However, existing upsample techniques leverage little information from downsampling paths. The local and detailed feature from the shallower layer such as boundary and tissue texture is crucial in segmentation, especially medical image segmentation. To this end, we propose a novel upsample approach for medical image segmentation, Window Attention Upsample (WAU), which upsamples features conditioned on local and detailed features from downsampling path in local windows by introducing attention decoders of Transformer. WAU could serve as a general upsample method and be incorporated into any segmentation model that possesses lateral connections. We first propose the Attention Upsample which consists of Attention Decoder (AD) and bilinear upsample. AD leverages pixel-level attention to model longrange dependency and global information for a better upsample. Bilinear upsample is introduced as the residual connection to complement the upsampled features. Moreover, considering the extensive memory and computation cost of pixel-level attention, we further design a window attention scheme to restrict attention computation in local windows instead of the global range. We evaluate our method (WAU) on classic UNet structure with lateral connections and achieve state-of-the-art performance on Medical Segmentation Decathlon (MSD) Brain and Automatic Cardiac Diagnosis Challenge (ACDC) datasets. We also validate the effectiveness of our method on multiple classic architectures and achieve consistent improvement. © 2022 IEEE.

关键词： Diagnosis

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jianghao Xiong Jianhuang Lai School of Computer Science and Engineering Sun Yat-Sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Key Laboratory of Video and Image Intelligent Analysis and Applicaiton Technology Ministry of Public Security China

Group re-identification (G-ReID) aims to re-identify a group of people that is observed from non-overlapping camera systems. The existing literature has mainly addressed RGB-based problems, but RGB-infrared (RGB-IR) cross-modality matching problem has not been studied yet. In this paper, we propose a metric learning method Closest Permutation Matching (CPM) for RGB-IR G-ReID. We model each group as a set of single-person features which are extracted by MPANet, then we propose the metric Closest Permutation Distance (CPD) to measure the similarity between two sets of features. CPD is invariant with order changes of group members so that it solves the layout change problem in G-ReID. Furthermore, we introduce the problem of G-ReID without person labels. In the weak-supervised case, we design the Relation-aware Module (RAM) that exploits visual context and relations among group members to produce a modality-invariant order of features in each group, with which group member features within a set can be sorted to form a robust group representation against modality change. To support the study on RGB-IR G-ReID, we construct a new large-scale RGB-IR G-ReID dataset CM-Group. The dataset contains 15,440 RGB images and 15,506 infrared images of 427 groups and 1,013 identi-ties. Extensive experiments on the new dataset demonstrate the effectiveness of the proposed models and the complexity of CM-Group. The code and dataset are available at: https://***/WhollyOat/CM-Group.

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model

arXiv

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arXiv 2024年

作者： Jiang, Lifan Wang, Zhihui Wang, Changmiao Li, Ming Leng, Jiaxu Wu, Xindong the College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266510 China Shenzhen Research Institute of Big Data Shenzhen518172 China Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China the Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China the Key Laboratory of Knowledge Engineering with Big Data the Ministry of Education of China China School of Computer Science and Information Technology Hefei University of Technology Hefei230009 China

Object detection, a quintessential task in the realm of perceptual computing, can be tackled using a generative methodology. In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities. This framework, termed ConsistencyDet, leverages an innovative denoising concept known as the Consistency Model. The hallmark of this model is its self-consistency feature, which empowers the model to map distorted information from any temporal stage back to its pristine state, thereby realizing a "one-step denoising" mechanism. Such an attribute markedly elevates the operational efficiency of the model, setting it apart from the conventional Diffusion Model. Throughout the training phase, ConsistencyDet initiates the diffusion sequence with noise-infused boxes derived from the ground-truth annotations and conditions the model to perform the denoising task. Subsequently, in the inference stage, the model employs a denoising sampling strategy that commences with bounding boxes randomly sampled from a normal distribution. Through iterative refinement, the model transforms an assortment of arbitrarily generated boxes into definitive detections. Comprehensive evaluations employing standard benchmarks, such as MS-COCO and LVIS, corroborate that ConsistencyDet surpasses other leading-edge detectors in performance metrics. Our code is available at https://***/Tankowa/ConsistencyDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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More than Encoder: Introducing Transformer Decoder to Upsample

arXiv

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arXiv 2021年

作者： Li, Yijiang Cai, Wentian Gao, Ying Li, Chengming Hu, Xiping School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China School of Medical Technology Beijing Institute of Technology Beijing China

Medical image segmentation methods downsample images for feature extraction and then upsample them to restore resolution for pixel-level predictions. In such schema, upsample technique is vital in restoring information for better performance. However, existing upsample techniques leverage little information from downsampling paths. The local and detailed feature from the shallower layer such as boundary and tissue texture is particularly more important in medical segmentation compared with natural image segmentation. To this end, we propose a novel upsample approach for medical image segmentation, Window Attention Upsample (WAU), which upsamples features conditioned on local and detailed features from downsampling path in local windows by introducing attention decoders of Transformer. WAU could serve as a general upsample method and be incorporated into any segmentation model that possesses lateral connections. We first propose the Attention Upsample which consists of Attention Decoder (AD) and bilinear upsample. AD leverages pixel-level attention to model long-range dependency and global information for a better upsample. Bilinear upsample is introduced as the residual connection to complement the upsampled features. Moreover, considering the extensive memory and computation cost of pixel-level attention, we further design a window attention scheme to restrict attention computation in local windows instead of the global range. We evaluate our method (WAU) on classic U-Net structure with lateral connections and achieve state-of-the-art performance on Synapse multi-organ segmentation, Medical Segmentation Decathlon (MSD) Brain, and Automatic Cardiac Diagnosis Challenge (ACDC) datasets. We also validate the effectiveness of our method on multiple classic architectures and achieve consistent improvement. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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