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Your search keyword '"Chang, Faliang"' showing total 215 results
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215 results on '"Chang, Faliang"'

51. Target Tracking Under Occlusion by Combining Integral-Intensity-Matching with Multi-block-voting

Author

Chang, Faliang, Ma, Li, Qiao, Yizheng, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, De-Shuang, editor, Zhang, Xiao-Ping, editor, and Huang, Guang-Bin, editor

Published
2005
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58. A Self-Trained Spatial Graph Convolutional Network for Unsupervised Human-Related Anomalous Event Detection in Complex Scenes

Author

Li, Nanjun, Chang, Faliang, and Liu, Chunsheng

Abstract

Most of previous works on abnormal event detection take this task as a novelty detection problem, which employ the supervised setting that needs videos containing only normal events for learning normal patterns. However, few works are developed under the unsupervised setting that detects anomaly without labeled normal videos. In this article, we develop a novel unsupervised algorithm using the skeleton feature for detecting human-related anomalous events. Our method applies the idea of self-training regression for iteratively updating the anomaly scores of skeletons for anomaly detection. In detail, each extracted skeleton is first decomposed into global and local feature components. Then, an unsupervised anomaly detector is operated on these two components to generate the initial anomalous and normal skeleton sets. These two sets are utilized to optimize parameters of an anomaly scoring module consisting of a spatial graph convolutional network (SGCN) and fully connected layers. The trained module is then employed to recalculate anomaly scores of all skeletons to update memberships of pseudo anomalous and normal skeletons set for the next training procedure, and this process is performed in an iterative way to get superior anomaly detection performance. Experimental results on two challenging data sets and their subsets that only contain human-related anomalies demonstrate our method outperforms several state-of-the-art supervised methods.

Published
2023
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59. Temporal Shift and Spatial Attention-Based Two-Stream Network for Traffic Risk Assessment.

Author

Liu, Chunsheng, Li, Zijian, Chang, Faliang, Li, Shuang, and Xie, Jincan

Abstract

On-board vision based traffic risk assessment is a challenging task for intelligent driving systems, which has some special challenges including spatial-temporal feature extraction, different judgements of risks, real-time requirement, lacking data, etc. To overcome these difficulties, we propose a novel Temporal Shift and Spatial Attention based Two-stream Network (TSSAT-Net) for on-board vision based traffic risk assessment. Firstly, we build new judgement measures that integrate actual driving experience and scenario complexity, and release an on-board vision based traffic risk assessment dataset. Secondly, a novel weighted Temporal Shift Module (weighted-TSM) based two-stream network is proposed; unlike previous methods that rely on complex and time-consuming 3D CNN or LSTM calculation, the proposed two-stream network can effectively extract spatial-temporal features using a weighted temporal shift mechanism with just 2D CNN calculation requirement. Thirdly, a spatial and channel attention mechanism is proposed to make the TSSAT-Net more focus on the features closely related to traffic risks, avoiding redundant information in complex traffic scenarios. Experiments based on the released comprehensive dataset show that our method achieves the state-of-the-art classification accuracy in real-time. [ABSTRACT FROM AUTHOR]

Published
2022
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76. Bi-Directional Dense Traffic Counting Based on Spatio-Temporal Counting Feature and Counting-LSTM Network.

Author

Li, Shuang, Chang, Faliang, and Liu, Chunsheng

Abstract

Machine vision based vehicle counting and traffic flow estimation are challenging problems especially for dense traffic scenarios. Previous line of interest (LOI) counting methods rarely focus on dense scenarios and their performance largely relies on the accuracy of tracking. Avoiding the use of complex tracking methods, an LOI counting framework is proposed to address the bi-directional LOI counting problem in dense scenarios. There are three main contributions. Firstly, instead of treating the LOI vehicle counting problem as a combination of detecting and tracking of individual vehicles, the bi-directional traffic flow is taken as a whole and a novel spatio-temporal counting feature (STCF) is proposed for extracting bi-directional traffic flow features in dense traffic scenarios. Secondly, without relying on a multi-target tracking process for tracking and counting each vehicle, a counting network is proposed, called the counting Long Short-Term Memory (cLSTM) network, to do analysis of the bi-directional STCF features and vehicle counting in successive video frames. Lastly, an estimation model is designed for estimating traffic flow parameters including speed, volume and density. Experiments performed on the UA-DETRAC dataset and the captured videos show that the proposed vehicle counting method outperforms the tested representative LOI counting methods in both accuracy and speed, and that the proposed framework can efficiently estimate traffic flow parameters including speed, volume and density in real time. [ABSTRACT FROM AUTHOR]

Published
2021
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78. Multi-Scale and Occlusion Aware Network for Vehicle Detection and Segmentation on UAV Aerial Images

Author

Chang Faliang, Ye Song, Wang Zhang, and Chunsheng Liu

Subjects
feature pyramid network, 010504 meteorology & atmospheric sciences, Computer science, Science, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, Background noise, Market segmentation, self-attention mechanism, Vehicle detection, Occlusion, Segmentation, Computer vision, Pyramid (image processing), 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, vehicle segmentation, vehicle detection, aerial images, business.industry, Feature (computer vision), General Earth and Planetary Sciences, Artificial intelligence, Scale (map), business
Abstract

With the advantage of high maneuverability, Unmanned Aerial Vehicles (UAVs) have been widely deployed in vehicle monitoring and controlling. However, processing the images captured by UAV for the extracting vehicle information is hindered by some challenges including arbitrary orientations, huge scale variations and partial occlusion. In seeking to address these challenges, we propose a novel Multi-Scale and Occlusion Aware Network (MSOA-Net) for UAV based vehicle segmentation, which consists of two parts including a Multi-Scale Feature Adaptive Fusion Network (MSFAF-Net) and a Regional Attention based Triple Head Network (RATH-Net). In MSFAF-Net, a self-adaptive feature fusion module is proposed, which can adaptively aggregate hierarchical feature maps from multiple levels to help Feature Pyramid Network (FPN) deal with the scale change of vehicles. The RATH-Net with a self-attention mechanism is proposed to guide the location-sensitive sub-networks to enhance the vehicle of interest and suppress background noise caused by occlusions. In this study, we release a large comprehensive UAV based vehicle segmentation dataset (UVSD), which is the first public dataset for UAV based vehicle detection and segmentation. Experiments are conducted on the challenging UVSD dataset. Experimental results show that the proposed method is efficient in detecting and segmenting vehicles, and outperforms the compared state-of-the-art works.

Published
2020

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