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2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)最新文献

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Multiple object tracking based on motion segmentation of point trajectories 基于点轨迹运动分割的多目标跟踪
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2016-08-01 DOI: 10.1109/AVSS.2016.7738057
N. Dimitriou, G. Stavropoulos, K. Moustakas, D. Tzovaras
{"title":"Multiple object tracking based on motion segmentation of point trajectories","authors":"N. Dimitriou, G. Stavropoulos, K. Moustakas, D. Tzovaras","doi":"10.1109/AVSS.2016.7738057","DOIUrl":"https://doi.org/10.1109/AVSS.2016.7738057","url":null,"abstract":"In this paper we propose an algorithm for multiple object tracking, a heavily researched but still challenging problem of computer vision. We follow the tracking by detection paradigm in an online fashion and formulate tracking as a typical assignment problem between detections and existing tracks that is solved by a modification of the Hungarian algorithm. Contrary to other methods that use a multitude of features based on appearance, optical flow and prior knowledge gained through training, we solely use clusters of point trajectories to link detections and tracks. Point trajectories are robust under partial occlusions and allow the expansion of a track even in the absence of a detection. At the core of our algorithm lies a motion segmentation method that extracts coherent clusters from triangulated point trajectories. Our algorithm achieves competitive results on the 2D MOT 2015 benchmark showcasing its potential.","PeriodicalId":438290,"journal":{"name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131606519","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
Efficient large-scale photometric reconstruction using Divide-Recon-Fuse 3D Structure from Motion 基于运动的分割-识别-融合三维结构的高效大规模光度重建
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2016-08-01 DOI: 10.1109/AVSS.2016.7738070
Yueming Yang, Ming-Ching Chang, Longyin Wen, P. Tu, H. Qi, Siwei Lyu
{"title":"Efficient large-scale photometric reconstruction using Divide-Recon-Fuse 3D Structure from Motion","authors":"Yueming Yang, Ming-Ching Chang, Longyin Wen, P. Tu, H. Qi, Siwei Lyu","doi":"10.1109/AVSS.2016.7738070","DOIUrl":"https://doi.org/10.1109/AVSS.2016.7738070","url":null,"abstract":"We propose an efficient framework for large-scale 3D reconstruction from a large set of photos following the Structure-from-Motion (SfM) paradigm with divide-conquer and fusion. Our main novelty is to ensure commonality from overlaps between image sets corresponding to their reconstructions, which facilitates effective stitching and fusion. Specifically, such commonality is ensured by selecting a set of duplicated images (which are termed anchor images) in adjacent image sets prior to the 3D reconstruction. The anchor images can assist accurate fusion of the 3D point clouds. We describe an efficient RANSAC scheme for pairwise stitching. Our method is intuitively scalable to large site reconstruction via subdivision and fusion following a graph construct. We further describe another RANSAC algorithm to improve loop closure in our anchor image approach. Experimental results on reconstructing a large portion of a university campus demonstrate the efficacy of our method.","PeriodicalId":438290,"journal":{"name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"4052 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127550916","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
Human violence recognition and detection in surveillance videos 监控视频中人类暴力行为的识别与检测
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2016-08-01 DOI: 10.1109/AVSS.2016.7738019
P. Bilinski, F. Brémond
{"title":"Human violence recognition and detection in surveillance videos","authors":"P. Bilinski, F. Brémond","doi":"10.1109/AVSS.2016.7738019","DOIUrl":"https://doi.org/10.1109/AVSS.2016.7738019","url":null,"abstract":"In this paper, we focus on the important topic of violence recognition and detection in surveillance videos. Our goal is to determine if a violence occurs in a video (recognition) and when it happens (detection). Firstly, we propose an extension of the Improved Fisher Vectors (IFV) for videos, which allows to represent a video using both local features and their spatio-temporal positions. Then, we study the popular sliding window approach for violence detection, and we re-formulate the Improved Fisher Vectors and use the summed area table data structure to speed up the approach. We present an extensive evaluation, comparison and analysis of the proposed improvements on 4 state-of-the-art datasets. We show that the proposed improvements make the violence recognition more accurate (as compared to the standard IFV, IFV with spatio-temporal grid, and other state-of-the-art methods) and make the violence detection significantly faster.","PeriodicalId":438290,"journal":{"name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121824733","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: 82
Novel dataset for fine-grained abnormal behavior understanding in crowd 群体中细粒度异常行为理解的新数据集
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2016-08-01 DOI: 10.1109/AVSS.2016.7738074
H. Rabiee, J. Haddadnia, Hossein Mousavi, M. Kalantarzadeh, Moin Nabi, Vittorio Murino
{"title":"Novel dataset for fine-grained abnormal behavior understanding in crowd","authors":"H. Rabiee, J. Haddadnia, Hossein Mousavi, M. Kalantarzadeh, Moin Nabi, Vittorio Murino","doi":"10.1109/AVSS.2016.7738074","DOIUrl":"https://doi.org/10.1109/AVSS.2016.7738074","url":null,"abstract":"Despite the huge research on crowd on behavior understanding in visual surveillance community, lack of publicly available realistic datasets for evaluating crowd behavioral interaction led not to have a fair common test bed for researchers to compare the strength of their methods in the real scenarios. This work presents a novel crowd dataset contains around 45,000 video clips which annotated by one of the five different fine-grained abnormal behavior categories. We also evaluated two state-of-the-art methods on our dataset, showing that our dataset can be effectively used as a benchmark for fine-grained abnormality detection. The details of the dataset and the results of the baseline methods are presented in the paper.","PeriodicalId":438290,"journal":{"name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132944329","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: 50
An energy-efficient wireless video sensor node with a region-of-interest based multi-parameter rate controller for moving object surveillance 一种基于感兴趣区域的多参数速率控制器的高能效无线视频传感器节点
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2016-08-01 DOI: 10.1109/AVSS.2016.7738054
J. Ko, Taesik Na, S. Mukhopadhyay
{"title":"An energy-efficient wireless video sensor node with a region-of-interest based multi-parameter rate controller for moving object surveillance","authors":"J. Ko, Taesik Na, S. Mukhopadhyay","doi":"10.1109/AVSS.2016.7738054","DOIUrl":"https://doi.org/10.1109/AVSS.2016.7738054","url":null,"abstract":"This paper presents a lightweight video sensor node for moving object surveillance using region-of-interest (ROI) based coding and an on-line multi-parameter rate controller. The proposed ROI-based coding scheme determines ROI blocks, pre-processes non-ROI blocks using bit-truncation, and encodes all blocks using Motion JPEG. The on-line rate controller modulates the parameters of the ROI-based coding scheme to match the encoded data rate and transmission data rate under the variations in channel bandwidth and input video content. The low-complexity hardware of the ROI-based coding scheme reduces computation energy, and the on-line rate controller minimizes buffer requirement. The sensor node is designed in 130nm CMOS and prototyped in a Virtex-V FPGA. Simulations show that, under the same ROI quality, the proposed approach reduces system energy by 61% compared to H.264/AVC.","PeriodicalId":438290,"journal":{"name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134485718","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: 8
Group activity recognition on outdoor scenes 户外场景群体活动识别
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2016-08-01 DOI: 10.1109/AVSS.2016.7738071
Kyle Stephens, A. Bors
{"title":"Group activity recognition on outdoor scenes","authors":"Kyle Stephens, A. Bors","doi":"10.1109/AVSS.2016.7738071","DOIUrl":"https://doi.org/10.1109/AVSS.2016.7738071","url":null,"abstract":"In this research study, we propose an automatic group activity recognition approach by modelling the interdependencies of group activity features over time. Unlike in simple human activity recognition approaches, the distinguishing characteristics of group activities are often determined by how the movement of people are influenced by one another. We propose to model the group interdependences in both motion and location spaces. These spaces are extended to time-space and time-movement spaces and modelled using Kernel Density Estimation (KDE). Such representations are then fed into a machine learning classifier which identifies the group activity. Unlike other approaches to group activity recognition, we do not rely on the manual annotation of pedestrian tracks from the video sequence.","PeriodicalId":438290,"journal":{"name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"61 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116354392","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: 6
Design space exploration for adaptive privacy protection in airborne images 航空图像自适应隐私保护的设计空间探索
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2016-08-01 DOI: 10.1109/AVSS.2016.7738067
Omair Sarwar, B. Rinner, A. Cavallaro
{"title":"Design space exploration for adaptive privacy protection in airborne images","authors":"Omair Sarwar, B. Rinner, A. Cavallaro","doi":"10.1109/AVSS.2016.7738067","DOIUrl":"https://doi.org/10.1109/AVSS.2016.7738067","url":null,"abstract":"Airborne cameras on low-flying unmanned vehicles introduce new privacy challenges due to their mobility and viewing angles. In this paper, we focus on face recognition from airborne cameras and explore the design space to determine when a face in an airborne image is inherently protected, that is when an individual is not recognizable. Moreover, when individuals are recognizable by facial recognition algorithms, we propose an adaptive filtering mechanism to lower the face resolution in order to preserve privacy while ensuring a minimum reduction of the fidelity of the image. In particular, we estimate the resolution of faces captured at different altitudes and tilt angles using the data from navigation sensors and ascertain when the captured face is inherently protected. When the face is unprotected, we define a mechanism that automatically configures the strength of a privacy protection filter to improve the trade-off between privacy protection and fidelity of an aerial image or video.","PeriodicalId":438290,"journal":{"name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128216856","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: 15
Kernel Partial Least Squares for person re-identification 核偏最小二乘法用于人的再识别
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2016-08-01 DOI: 10.1109/AVSS.2016.7738030
Raphael C. Prates, Marina Oliveira, W. R. Schwartz
{"title":"Kernel Partial Least Squares for person re-identification","authors":"Raphael C. Prates, Marina Oliveira, W. R. Schwartz","doi":"10.1109/AVSS.2016.7738030","DOIUrl":"https://doi.org/10.1109/AVSS.2016.7738030","url":null,"abstract":"Person re-identification (Re-ID) keeps the same identity for a person as he moves along an area with nonoverlapping surveillance cameras. Re-ID is a challenging task due to appearance changes caused by different camera viewpoints, occlusion and illumination conditions. While robust and discriminative descriptors are obtained combining texture, shape and color features in a high-dimensional representation, the achievement of accuracy and efficiency demands dimensionality reduction methods. At this paper, we propose variations of Kernel Partial Least Squares (KPLS) that simultaneously reduce the dimensionality and increase the discriminative power. The Cross-View KPLS (X-KPLS) and KPLS Mode A capture cross-view discriminative information and are successful for unsupervised and supervised Re-ID. Experimental results demonstrate that X-KPLS presents equal or higher matching results when compared to other methods in literature at PRID450S.","PeriodicalId":438290,"journal":{"name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123046313","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: 16
Multi-object tracking of pedestrian driven by context 上下文驱动下行人的多目标跟踪
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2016-08-01 DOI: 10.1109/AVSS.2016.7738022
T. Nguyen, F. Brémond, J. Trojanová
{"title":"Multi-object tracking of pedestrian driven by context","authors":"T. Nguyen, F. Brémond, J. Trojanová","doi":"10.1109/AVSS.2016.7738022","DOIUrl":"https://doi.org/10.1109/AVSS.2016.7738022","url":null,"abstract":"The characteristics like density of objects, their contrast with respect to surrounding background, their occlusion level and many more describe the context of the scene. The variation of the context represents ambiguous task to be solved by tracker. In this paper we present a new long term tracking framework boosted by context around each tracklet. The framework works by first learning the database of optimal tracker parameters for various context offline. During the testing, the context surrounding each tracklet is extracted and match against database to select best tracker parameters. The tracker parameters are tuned for each tracklet in the scene to highlight its discrimination with respect to surrounding context rather than tuning the parameters for whole scene. The proposed framework is trained on 9 public video sequences and tested on 3 unseen sets. It outperforms the state-of-art pedestrian trackers in scenarios of motion changes, appearance changes and occlusion of objects.","PeriodicalId":438290,"journal":{"name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115969643","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: 9
Semi-supervised understanding of complex activities from temporal concepts 从时间概念对复杂活动的半监督理解
2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) Pub Date : 2016-08-01 DOI: 10.1109/AVSS.2016.7738029
C. Crispim, Michal Koperski, S. Coşar, F. Brémond
{"title":"Semi-supervised understanding of complex activities from temporal concepts","authors":"C. Crispim, Michal Koperski, S. Coşar, F. Brémond","doi":"10.1109/AVSS.2016.7738029","DOIUrl":"https://doi.org/10.1109/AVSS.2016.7738029","url":null,"abstract":"Methods for action recognition have evolved considerably over the past years and can now automatically learn and recognize short term actions with satisfactory accuracy. Nonetheless, the recognition of complex activities - compositions of actions and scene objects - is still an open problem due to the complex temporal and composite structure of this category of events. Existing methods focus either on simple activities or oversimplify the modeling of complex activities by targeting only whole-part relations between its sub-parts (e.g., actions). In this paper, we propose a semi-supervised approach that learns complex activities from the temporal patterns of concept compositions (e.g., “slicing-tomato” before “pouring into-pan”). We demonstrate that our method outperforms prior work in the task of automatic modeling and recognition of complex activities learned out of the interaction of 218 distinct concepts.","PeriodicalId":438290,"journal":{"name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128757201","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: 4
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