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A camera-based system for tracking people in real time

J. Segen
1996 Proceedings of 13th International Conference on Pattern Recognition  
This paper describes a s y s t e m f o r real-time tracking of people in video sequences.  ...  It identijies feature points in each video f r a m e , matches feature points across f r a m e s t o produce feature ')aths", t h e n groups short-lived and partially overlapping feature paths i n t o  ...  The system has been tested on several video sequences of people traffic in different indoor environments.  ... 
doi:10.1109/icpr.1996.546795 dblp:conf/icpr/Segen96 fatcat:vvi73h6ulbhorohd5imcgkqexm

Automatically Determining Dominant Motions in Crowded Scenes by Clustering Partial Feature Trajectories

Anil M. Cheriyadat, Richard J. Radke
2007 2007 First ACM/IEEE International Conference on Distributed Smart Cameras  
Results on real video sequences demonstrate that the approach can successfully identify both dominant and anomalous motions in crowded scenes.  ...  Our approach begins by independently tracking low-level features using optical flow.  ...  However, results on real video sequences demonstrate that the proposed clustering algorithm can identify both dominant and anomalous motions in crowded scenes by clustering these partial feature trajectories  ... 
doi:10.1109/icdsc.2007.4357505 dblp:conf/icdsc/CheriyadatR07 fatcat:d42vxyl6uvazpkj4wblvn7orxy

Detecting global motion patterns in complex videos

Min Hu, Saad Ali, Mubarak Shah
2008 Pattern Recognition (ICPR), Proceedings of the International Conference on  
In this paper, we propose an algorithm that uses instantaneous motion field of the video instead of long-term motion tracks for learning the motion patterns.  ...  We also use the super tracks for eventbased video matching. The efficacy of the approach is demonstrated on challenging real-world sequences.  ...  Acknowledgements: This research was funded by the US Government VACE program.  ... 
doi:10.1109/icpr.2008.4760950 dblp:conf/icpr/HuAS08 fatcat:uhcxw3i3krdxdpuah4wzoy32dq

A Novel Trajectory Clustering Approach for Motion Segmentation [chapter]

Matthias Zeppelzauer, Maia Zaharieva, Dalibor Mitrovic, Christian Breiteneder
2010 Lecture Notes in Computer Science  
We propose a novel clustering scheme for spatio-temporal segmentation of sparse motion fields obtained from feature tracking.  ...  Results show, that our method successfully segments the motion components even in particularly noisy sequences.  ...  [1] [2] [3] ) we extract motion trajectories by feature tracking directly from the raw video sequence and omit object segmentation.  ... 
doi:10.1007/978-3-642-11301-7_44 fatcat:ergaccp2tndrbilyti6lwxec6m

Using dynamic Bayesian network for scene modeling and anomaly detection

Imran N. Junejo
2009 Signal, Image and Video Processing  
In this paper, we address the problem of scene modeling for performing video surveillance.  ...  During the training phase, the input trajectories are used to identify different paths or routes commonly taken by the objects in a scene.  ...  [9] demonstrate the use of network tomography for statistical tracking of activities in a video sequence.  ... 
doi:10.1007/s11760-008-0099-7 fatcat:4522ilppcza5ja6t7b3a5apyhu

Learning Pedestrian Trajectories with Kernels

Elisa Ricci, Francesco Tobia, Gloria Zen
2010 2010 20th International Conference on Pattern Recognition  
We demonstrate the effectiveness of our method incorporating the learned motion model into a multiperson tracking algorithm and testing it on several video surveillance sequences.  ...  To model crossing paths we rely on a clustering algorithm based on Kernel K-means with a Dynamic Time Warping (DTW) kernel.  ...  Typical paths in our scenario. Figure 2 . 2 (a) Clustered trajectories. (b) Paths extracted tracking with (red) and without (blue) the learned motion model .a.  ... 
doi:10.1109/icpr.2010.45 dblp:conf/icpr/RicciTZ10 fatcat:m2sbqx5egzgq3lhyqdnjdzrfhu

Multi feature path modeling for video surveillance

I.N. Junejo, O. Javed, M. Shah
2004 Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.  
Finally, the curvature features capture discontinuities in velocity, acceleration, and position of the trajectory. We use real-world pedestrian sequences to demonstrate the practicality of our method.  ...  An envelope boundary and an average trajectory are computed for each path. During the testing phase we use three features for trajectory matching in a hierarchical fashion.  ...  An example of an unusual behavior might be a person walking in a region not used by most people, a car following a zigzag path, or a person running in a region where most people simply walk.  ... 
doi:10.1109/icpr.2004.1334359 dblp:conf/icpr/JunejoJS04 fatcat:ffsetzkc5zhc5fzbqv7fvr3voy

Spatio-temporal Discovery: Appearance + Behavior = Agent [chapter]

Prithwijit Guha, Amitabha Mukerjee, K. S. Venkatesh
2006 Lecture Notes in Computer Science  
Improved algorithms for video segmentation and tracking under occlusion enable us to construct models that characterize agents in terms of motion and interaction with other objects.  ...  Using uncalibrated camera images, we characterize objects discovered in the scene by their shape and motion attributes, and cluster these using agglomerative hierarchical clustering.  ...  In this work, we consider dynamic image sequences and use improved algorithms for video segmentation and tracking under occlusion to construct coherent motion histories and occlusion relations simulating  ... 
doi:10.1007/11949619_46 fatcat:dqkvsh4g5nb6rhrkhaouwpm54i

Toward Abnormal Trajectory and Event Detection in Video Surveillance

Serhan Cosar, Giuseppe Donatiello, Vania Bogorny, Carolina Garate, Luis Otavio Alvares, Francois Bremond
2017 IEEE transactions on circuits and systems for video technology (Print)  
In this paper we present a unified approach for abnormal behavior detection and group behavior analysis in video scenes.  ...  threats for cargo video surveillance [3] .  ...  After the clustering step, a tree is build with the sequence of clusters, and an anomalous trajectory is defined as a trajectory that matches a path in the tree of clusters with low probability.  ... 
doi:10.1109/tcsvt.2016.2589859 fatcat:rrp3f42tzzdbpipky4r54kzupu

Multi-Person Tracking by Multicut and Deep Matching [article]

Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Bernt Schiele
2016 arXiv   pre-print
In [1], we proposed a graph-based formulation that links and clusters person hypotheses over time by solving a minimum cost subgraph multicut problem.  ...  In this paper, we modify and extend [1] in three ways: 1) We introduce a novel local pairwise feature based on local appearance matching that is robust to partial occlusion and camera motion. 2) We perform  ...  boxes that possibly identify people in a video sequence.  ... 
arXiv:1608.05404v1 fatcat:3msnj777abfnveshtpwm5uoqmy

Multi-person Tracking by Multicut and Deep Matching [chapter]

Siyu Tang, Bjoern Andres, Mykhaylo Andriluka, Bernt Schiele
2016 Lecture Notes in Computer Science  
In Tang et al. (2015) , we proposed a graph-based formulation that links and clusters person hypotheses over time by solving a minimum cost subgraph multicut problem.  ...  In this paper, we modify and extend Tang et al. (2015) in three ways: (1) We introduce a novel local pairwise feature based on local appearance matching that is robust to partial occlusion and camera motion  ...  This work has been supported by the Max Planck Center for Visual Computing and Communication.  ... 
doi:10.1007/978-3-319-48881-3_8 fatcat:bpcnvnemqnbzjhkoyihhrn5j5m

Clustering Motion for Real-Time Optical Flow Based Tracking

Tobias Senst, Ruben Heras Evangelio, Ivo Keller, Thomas Sikora
2012 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance  
Good Features to Track, select points to be tracked based on appearance features such as cornerness and therefore neglecting the motion exhibited by the selected points.  ...  The number of trajectories assigned to each motion cluster is adapted by initializing and removing tracked points by means of feed-back.  ...  Optical flow is increasingly being used in video surveillance systems, e.g., in order to distinguish people by motion in crowds, for crowd analysis and activity monitoring.  ... 
doi:10.1109/avss.2012.20 dblp:conf/avss/SenstEKS12 fatcat:dcauwtuvejaujopnyetr7nevae

Instantly indexed multimedia databases of real world events

G.S. Pingali, A. Opalach, Y.D. Jean, I.B. Carlbom
2002 IEEE transactions on multimedia  
This system analyzes video from multiple cameras in real time and captures the activity of the players and the ball in the form of motion trajectories.  ...  Index Terms-Broadcast, content-based retrieval, Internet, television, tracking, video, vision, visual data mining, visualization. 1520-9210/02$17.00 © 2002 IEEE  ...  A feature path consists of a sequence of feature matches and indicates the motion of a feature over time.  ... 
doi:10.1109/tmm.2002.1017739 fatcat:676g7lxydndgldziaisfmlzvtm

Moving Pixels in Static Cameras: Detecting Dangerous Situations due to Environment or People [chapter]

Simone Calderara, Rita Cucchiara, Andrea Prati
2010 Studies in Computational Intelligence  
The second refers to the problem of detecting suspicious or abnormal people behaviors by means of people trajectory analysis in a multiple cameras video-surveillance scenario.  ...  image features, such as color information and texture energy computed by the means of the Wavelet transform.  ...  First, after the clustering, clusters cardinality naturally represents by definition how often a specific path occurs, thus allowing to classify the paths in abnormal (unfrequent) and normal (frequent)  ... 
doi:10.1007/978-3-642-11756-5_1 fatcat:rhlbtuv26nbifbndgc6z3qo64e

Crowded Scene Analysis: A Survey

Teng Li, Huan Chang, Meng Wang, Bingbing Ni, Richang Hong, Shuicheng Yan
2015 IEEE transactions on circuits and systems for video technology (Print)  
In the past few years, an increasing number of works on crowded scene analysis have been reported, covering different aspects including crowd motion pattern learning, crowd behavior and activity analysis  ...  We first provide the background knowledge and the available features related to crowded scenes.  ...  Tracklets are collected by tracking dense feature points from the video of crowded scenes, and motion patterns are then learned by clustering the tracklets. In Jodoin et al.  ... 
doi:10.1109/tcsvt.2014.2358029 fatcat:prgoh37gjfcl7n6dp2u6tsdoda
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