Hang Su, Hua Yang, Shibao Zheng, Yawen Fan, Sha Wei
{"title":"Crowd Event Perception Based on Spatio-temporal Viscous Fluid Field","authors":"Hang Su, Hua Yang, Shibao Zheng, Yawen Fan, Sha Wei","doi":"10.1109/AVSS.2012.32","DOIUrl":"https://doi.org/10.1109/AVSS.2012.32","url":null,"abstract":"Over the past decades, a wide attention has been paid to crowd control and management in intelligent video surveillance area. In this paper, the authors propose a novel spatiotemporal viscous fluid field to recognize large-scale crowd event with respect to both appearance and driven factor of crowd behavior. Firstly, a spatiotemporal variation matrix is proposed to exploit motion property of a crowd. In particular, the paper exploits characteristics of the matrix with eigenvalue decomposition algorithm and constructs an abstract fluid field to model the crowd motion pattern, which is denoted by spatiotemporal fluid field. Secondly, the paper proposes a spatiotemporal force field to exploit the interaction force between the pedestrians. Furthermore, the fluid and force field constructs a spatiotemporal viscous fluid field. Thirdly, after generating feature with bag of word model, the authors utilize latent Dirichlet allocation model to recognize crowd behavior. The experiments on PETS2009 and UMN datasets show that the proposed method has a better performance for large-scale crowd behavior perception in both robustness and effectiveness comparing with the conventional methods.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"1 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":"130015024","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}
Teng Xu, Peixi Peng, Xiaoyu Fang, Chi Su, Yaowei Wang, Yonghong Tian, Wei Zeng, Tiejun Huang
{"title":"Single and Multiple View Detection, Tracking and Video Analysis in Crowded Environments","authors":"Teng Xu, Peixi Peng, Xiaoyu Fang, Chi Su, Yaowei Wang, Yonghong Tian, Wei Zeng, Tiejun Huang","doi":"10.1109/AVSS.2012.91","DOIUrl":"https://doi.org/10.1109/AVSS.2012.91","url":null,"abstract":"In this paper, we present our detection, tracking and event recognition methods and the results for PETS 2012. First, ROIs (Regions of Interest) based on geometric constraints are utilized in single view detection to eliminate the negative influence of clutter environment. Then, an optimized observation model is applied to address the ID switching or tracking drifting problem in single view tracking. Third, we introduce the multi-view Bayesian network (MBN) to reduce the \"phantom\" phenomena which frequently happen in general multi-view detection tasks. At last, a motion-based event recognition method is proposed to handle the event recognition task. Experimental results on the PETS 2012 dataset indicate that our methods are very promising.","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":"134026803","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}
{"title":"Online Multi-person Tracking by Tracker Hierarchy","authors":"Jianming Zhang, Liliana Lo Presti, S. Sclaroff","doi":"10.1109/AVSS.2012.51","DOIUrl":"https://doi.org/10.1109/AVSS.2012.51","url":null,"abstract":"Tracking-by-detection is a widely used paradigm for multi-person tracking but is affected by variations in crowd density, obstacles in the scene, varying illumination, human pose variation, scale changes, etc. We propose an improved tracking-by-detection framework for multi-person tracking where the appearance model is formulated as a template ensemble updated online given detections provided by a pedestrian detector. We employ a hierarchy of trackers to select the most effective tracking strategy and an algorithm to adapt the conditions for trackers' initialization and termination. Our formulation is online and does not require calibration information. In experiments with four pedestrian tracking benchmark datasets, our formulation attains accuracy that is comparable to, or better than, the state-of-the-art pedestrian trackers that must exploit calibration information and operate offline.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"41 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":"134338676","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}
{"title":"Detection, Segmentation, and Tracking of Moving Objects in UAV Videos","authors":"Michael Teutsch, W. Krüger","doi":"10.1109/AVSS.2012.36","DOIUrl":"https://doi.org/10.1109/AVSS.2012.36","url":null,"abstract":"Automatic processing of videos coming from small UAVs offers high potential for advanced surveillance applications but is also very challenging. These challenges include camera motion, high object distance, varying object background, multiple objects near to each other, weak signal-to-noise-ratio (SNR), or compression artifacts. In this paper, a video processing chain for detection, segmentation, and tracking of multiple moving objects is presented dealing with the mentioned challenges. The fundament is the detection of local image features, which are not stationary. By clustering these features and subsequent object segmentation, regions are generated representing object hypotheses. Multi-object tracking is introduced using a Kalman filter and considering the camera motion. Split or merged object regions are handled by fusion of the regions and the local features. Finally, a quantitative evaluation of object segmentation and tracking is provided.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"36 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":"132702414","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}
{"title":"Road Boundary Detection in Challenging Scenarios","authors":"M. Helala, K. Pu, F. Qureshi","doi":"10.1109/AVSS.2012.61","DOIUrl":"https://doi.org/10.1109/AVSS.2012.61","url":null,"abstract":"This paper presents a new approach for automatic road detection in traffic cameras. The technique proposed here detects the dominant road boundary and estimates the vanishing point in images captured by traffic cameras under a wide range of lighting and environmental conditions, e.g., in images of unlit highways captured at night, etc. The approach starts by segmenting the traffic scene into a number of superpixel regions. The contours of these regions are used to generate a large number of edges which are organized into clusters of co-linearly similar sets using hierarchical bottom up clustering. A confidence level is assigned to each cluster using a statistical approach and the best clusters are chosen. Pairs of clusters with high confidence levels are then ranked and filtered according to image perspective and activity. The top ranked pair is selected as the road boundary. The proposed technique is tested on a real world dataset collected from the Ontario 401 traffic surveillance system. Experimental results demonstrate a distinct speedup and improvement in accuracy of the proposed technique in detecting the dominant road boundary in challenging scenarios compared to the state of the art Gabor filter based technique.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"20 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":"123382289","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}
{"title":"Probabilistic Handling of Merged Detections in Multi Target Tracking","authors":"T. Stephan, M. Grinberg","doi":"10.1109/AVSS.2012.56","DOIUrl":"https://doi.org/10.1109/AVSS.2012.56","url":null,"abstract":"In this contribution we present a method for handling merged detections in video-based Multi-Target-Tracking applications. Merged detections occur when two or more objects evoke one joint detection, i.e. when measurements stemming from multiple objects cannot be resolved by the sensor. In video-based applications this is the case when the segmentation fails to separate blobs belonging to different objects. The proposed approach is based on the specification of candidate merge events and resulting data association events. We propose a concept which allows recognition of the merge events and a correct track update in case of identified merges. This is done by generating artificial (virtual) measurements (measurement reconstruction) through decomposition of respective detections. The globally optimal solution is achieved by weighting different candidate hypotheses according to their a-posteriori probabilities.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"33 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":"128801964","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}
{"title":"Pairwise Threshold for Gaussian Mixture Classification and Its Application on Human Tracking Enhancement","authors":"Daegeon Kim, S. Lee","doi":"10.1109/AVSS.2012.53","DOIUrl":"https://doi.org/10.1109/AVSS.2012.53","url":null,"abstract":"In this paper, we describe Object Pixel Mixture Classifiers (OPMCs) which classify an object not only apart from background but also from other objects based on Gaussian Mixture Model (GMM) classification. The proposed OPMC is different from general GMM based classifiers in the respect that novel pairwise threshold is applied for final classification. Pairwise thresholds are different thresholds depending on predicted mixture component index combination by a positive and a negative GMMs. We train the pairwise threshold using discriminative model so that generative GMM can take advantage from it. We demonstrate that OPMCs are robust to noise in train data and can keep tracking objects after missing tracks even with occlusion. Also, we show that OPMCs can generate meaningful blob of object, and can separate the region of objects from merged blobs.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"45 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":"125024217","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}
{"title":"Suppression of Detection Ghosts in Homography Based Pedestrian Detection","authors":"M. Evans, Longzhen Li, J. Ferryman","doi":"10.1109/AVSS.2012.73","DOIUrl":"https://doi.org/10.1109/AVSS.2012.73","url":null,"abstract":"One popular approach for multi-camera detection of pedestrians or other objects of interest in surveillance scenes is to perform background subtraction and project the resulting foreground mask images to a common scene plane using homographies. As the complexity of the scene increases, it is unavoidable that so called \"ghost\" detections should occur. These are false positives, indicating the presence of an object of interest where no such object actually exists. This paper proposes an approach to predicting where these ghost detections will occur, and provides a mechanism for suppressing their appearance.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"4 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":"127187539","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}
{"title":"Adaptive Autoregressive Logarithmic Search for 3D Human Tracking","authors":"Peiyao Li, A. Bouzerdoum, S. L. Phung","doi":"10.1109/AVSS.2012.7","DOIUrl":"https://doi.org/10.1109/AVSS.2012.7","url":null,"abstract":"Human tracking is an important vision task in video surveillance and perceptual human-computer interfaces. This paper presents a novel algorithm for region-based human tracking using color and depth features. We propose an adaptive autoregressive logarithmic search (ARLS) to estimate the target position, and use depth information to further reduce the false alarm rate. The new ARLS algorithm is evaluated on a color and depth (RGBD) video dataset acquired with the Kinect sensor. The dataset contains various real-world scenarios with illumination and speed variations, and partial occlusion. The experimental results show that the ARLS algorithm is able to handle difficult tracking scenarios, it achieves a tracking accuracy of 91.26% on the test dataset. The proposed algorithm is compared with two tracking algorithms, namely the particle filtering and a modified logarithmic search algorithm.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"95 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":"128583960","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}
{"title":"Boosting Multi-hypothesis Tracking by Means of Instance-Specific Models","authors":"Michael Pätzold, Rubén Heras Evangelio, T. Sikora","doi":"10.1109/AVSS.2012.18","DOIUrl":"https://doi.org/10.1109/AVSS.2012.18","url":null,"abstract":"In this paper we present a visual person tracking-by-detection system based on on-line-learned instance-specific information along with the kinematic relation of measurements provided by a generic person-category detector. The proposed system is able to initialize tracks on individual persons and start learning their appearance even in crowded situations and does not require that a person enters the scene separately. For that purpose we integrate the process of learning instance-specific models into a standard MHT-framework. The capability of the system to eliminate detections-to-object association ambiguities occurring from missed detections or false ones is demonstrated by experiments for counting and tracking applications using very long video sequences on challenging outdoor scenarios.","PeriodicalId":275325,"journal":{"name":"2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance","volume":"2012 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":"114872791","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}