2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance最新文献_第8页

String Features: Geodesic Sweeping Detection and Quasi-invariant Time-Series Description 字符串特征:测地线扫描检测和准不变时间序列描述

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.72

Gutemberg Guerra-Filho

引用次数: 0

Role of Spatiotemporal Oriented Energy Features for Robust Visual Tracking in Video Surveillance 面向时空的能量特征在视频监控中鲁棒视觉跟踪中的作用

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.64

Ali Emami, F. Dadgostar, A. Bigdeli, B. Lovell

引用次数: 23

CLUMOC: Multiple Motion Estimation by Cluster Motion Consensus CLUMOC:基于聚类运动一致性的多运动估计

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.19

Yinan Yu, Weiqiang Ren, Yongzhen Huang, Kaiqi Huang, T. Tan

引用次数: 0

Multimodal and Multi-task Audio-Visual Vehicle Detection and Classification 多模式多任务视听车辆检测与分类

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.47

Tao Wang, Zhigang Zhu

{"title":"Multimodal and Multi-task Audio-Visual Vehicle Detection and Classification","authors":"Tao Wang, Zhigang Zhu","doi":"10.1109/AVSS.2012.47","DOIUrl":"https://doi.org/10.1109/AVSS.2012.47","url":null,"abstract":"Moving vehicle detection and classification using multimodal data is a challenging task in data collection, audio-visual alignment, and feature selection, and effective vehicle classification in uncontrolled environments. In this work, we first present a systematic way to align the multimodal data based the multimodal temporal panorama generation. Then various types of features are extracted to represent diverse and multimodal information. Those include global geometric features (aspect ratios, profiles), local structure features (HOGs), various audio features in both spectral and perceptual representations. A flexible sequential forward selection algorithm with multi-branch searching is used to select a set of important features at different levels of feature combinations. Finally, using the same datasets for two different classification tasks, we show that the roles of audio and visual features are task-specific. Furthermore, in both cases, the combination of some of the features with multimodal and complementary information can improve the accuracy than using the individual features only. Therefore finer and more accurate classification can be achieved by two different levels of integration: feature level and the decision level.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123027462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

Human Action Recognition with Attribute Regularization 基于属性正则化的人体动作识别

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.41

Zhong Zhang, Chunheng Wang, Baihua Xiao, Wen Zhou, Shuang Liu

引用次数: 0

Robust Traffic State Estimation on Smart Cameras 智能摄像机的鲁棒交通状态估计

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-18 DOI: 10.1109/AVSS.2012.63

Felix Pletzer, R. Tusch, L. Böszörményi, B. Rinner

引用次数: 12

Analyzing the Subspaces Obtained by Dimensionality Reduction for Human Action Recognition from 3d Data 基于降维的人体动作识别子空间分析

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-01 DOI: 10.1109/AVSS.2012.10

Marco Körner, Joachim Denzler

引用次数: 6

Improved Relational Feature Model for People Detection Using Histogram Similarity Functions 基于直方图相似函数的改进关系特征模型

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-01 DOI: 10.1109/AVSS.2012.42

A. Zweng, M. Kampel

{"title":"Improved Relational Feature Model for People Detection Using Histogram Similarity Functions","authors":"A. Zweng, M. Kampel","doi":"10.1109/AVSS.2012.42","DOIUrl":"https://doi.org/10.1109/AVSS.2012.42","url":null,"abstract":"In this paper, we propose a new approach for people detection using a relational feature model (RFM) in combination with histogram similarity functions such as the bhattacharyya distance, histogram intersection, histogram correlation and the chi-square χ2 histogram similarity function. The relational features are computed for all combinations of extracted features from a feature detection algorithm such as the Histograms of Oriented Gradients (HOG) feature descriptor. Our experiments show, that the information of spatial histogram similarities reduces the number of false positives while preserving true positive detections. The detection algorithm is done, using a multi-scale overlapping sliding window approach. In our experiments, we show results for different sizes of the cell size from the HOG descriptor due to the large size of the resulting relational feature vector as well as different results from the mentioned histogram similarity functions. Additionally our results show, that in addition to less false positives, true positive responses in regions near people are much more accurate using the relational features compared to non-relational feature models.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115956941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Moving Object Extraction Using Compressed Domain Features of H.264 INTRA Frames 利用H.264 INTRA帧压缩域特征提取运动目标

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-09-01 DOI: 10.1109/AVSS.2012.46

Fu-Ping Wang, W. Chung, Guo-Kai Ni, Ing-Yi Chen, S. Kuo

{"title":"Moving Object Extraction Using Compressed Domain Features of H.264 INTRA Frames","authors":"Fu-Ping Wang, W. Chung, Guo-Kai Ni, Ing-Yi Chen, S. Kuo","doi":"10.1109/AVSS.2012.46","DOIUrl":"https://doi.org/10.1109/AVSS.2012.46","url":null,"abstract":"A new efficient algorithm using the compressed domain features of H.264 INTRA frames is proposed for moving object extraction on huge video surveillance archives. To achieve searching efficiency, we propose to locate moving objects by scrutinizing only the INTRA frames in video surveillance archives in H.264 compressed domain with short GOP length. In the proposed structure, a modified codebook algorithm is designed to build the block-based background models from the INTRA coding features. Through the subtraction with the background codebook models, the foreground energy frame is filtered and normalized for detecting the existence of moving objects. To overcome the over-segmentation problem and enable the unsupervised searching, a new structure of hysteresis thresholding, where the thresholds are obtained automatically by an efficient algorithm, is adopted to extract foreground blocks. At the final step, the connected components labeling (CCL) and morphological filters are employed to obtain the list of moving objects. As shown in the experimental results, the proposed algorithm outperforms representative existing works.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133720909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Activity Analysis in Complicated Scenes Using DFT Coefficients of Particle Trajectories 基于粒子轨迹DFT系数的复杂场景活动性分析

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance Pub Date : 2012-06-25 DOI: 10.1109/AVSS.2012.6

Jingxin Xu, S. Denman, S. Sridharan, C. Fookes

引用次数: 12