Evolving networks for group object motion estimation (original) (raw)
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Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods
IEEE Transactions on Signal Processing, 2000
This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the groups. Each node of the graph corresponds to a target within the group. The uncertainty of the group structure is estimated jointly with the group target states. New group structure evolving models are proposed for automatic graph structure initialisation, incorporation of new nodes, unexisting nodes removal and the edge update. Both the state and the graph structure are updated based on range and bearing measurements. This evolving graph model is propagated combined with a sequential Monte Carlo framework able to cope with measurement origin uncertainty. The effectiveness of the proposed approach is illustrated over scenarios for group motion estimation in urban environments. Results with challenging scenarios with merging, splitting and crossing of groups are presented with high estimation accuracy. The performance of the algorithm is also evaluated and shown on real ground moving target indicator (GMTI) radar data and in the presence of data origin uncertainty.
Tracking Groups of Pedestrians in Video Sequences
2003
This paper describes an algorithm for tracking groups of objects in video sequences. The main difficulties addressed in this work concern total occlusions of the objects to be tracked as well as group merging and splitting. A two layer solution is proposed to overcome these difficulties. The first layer produces a set of spatio temporal strokes based on low level operations which manage to track the active regions most of the time. The second layer performs a consistent labeling of the detected segments using a statistical model based on Bayesian networks. The Bayesian network is recursively computed during the tracking operation and allows the update of the tracker results everytime new information is available. Experimental tests are included to show the performance of the algorithm in ambiguous situations. * this work was partially supported by FCT and POCTI in the scope of project LTT 37844.
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation - SCA '05, 2005
We introduce Group Motion Graphs, a data-driven animation technique for groups of discrete agents, such as flocks, herds, or small crowds. Group Motion Graphs are conceptually similar to motion graphs constructed from motion-capture data, but have some important differences: we assume simulated motion; transition nodes are found by clustering group configurations from the input simulations; and clips to join transitions are explicitly constructed via constrained simulation. Graphs built this way offer known bounds on the trajectories that they generate, making it easier to search for particular output motions. The resulting animations show realistic motion at significantly reduced computational cost compared to simulation, and improved control.
Group object structure and state estimation in the presence of measurement origin uncertainty
2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 2009
This paper proposes a technique for motion and group structure estimation of moving targets based on evolving graph networks in the presence of measurement origin uncertainty. The proposed method, through an evolving graph model, allows to jointly estimate the group target and the group structure with the uncertainty. The performance of the algorithm is evaluated and results with real ground
Video Based Group Tracking and Management
Lecture Notes in Computer Science, 2016
Tracking objects in video is a very challenging research topic, particularly when people in groups are tracked, with partial and full occlusions and group dynamics being common difficulties. Hence, its necessary to deal with group tracking, formation and separation, while assuring the overall consistency of the individuals. This paper proposes enhancements to a group management and tracking algorithm that receives information of the persons in the scene, detects the existing groups and keeps track of the persons that belong to it. Since input information for group management algorithms is typically provided by a tracking algorithm and it is affected by noise, mechanisms for handling such noisy input tracking information were also successfully included. Performed experiments demonstrated that the described algorithm outperformed state-of-the-art approaches.
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)
Attribute graphs offer a compact representation of 2D or 3D images, as each node represents a region with its attributes and the edges convey the neighborhood relations between adjacent regions. Such graphs may be used in the analysis of video sequences and the tracking of objects of interest. Each image of a sequence is segmented and represented as a a region adjacency graph. Object tracking becomes a particular graph-matching problem, in which the nodes representing the same object are to be matched. The intrinsic complexity of graph matching is greatly reduced by coupling it with the segmentation. An attractive feature of our approach is its ability to keep track of occluded objects. Results on real sequences show the potential of this approach.
Identification and Tracking of Groups of People using Object Detection and Object Tracking
International Journal on Advances in ICT for Emerging Regions (ICTer)
Object detection is one of the most important areas in the fields of Data Science and Computer Vision. In this paper, we present a novel approach to identifying and tracking groups of people, couples, and individuals in videos by using deep learning-based object detection and object tracking techniques along with a proposed grouping algorithm. In this approach, transfer learning is applied on YOLO v3 model for the detection of people in video frames, and Deep SORT is applied for tracking each detected person throughout the video. Results obtained from person detection and person tracking were used by the proposed grouping algorithm to identify and track groups, couples, and individuals who are appearing in input videos. Our proposed grouping algorithm is based on the proximity between each individual and the time duration that proximity is maintained for. It also considers how to identify and track groups, when people are moving within the groups. This approach was evaluated using C...
Graph Mining for Object Tracking in Videos
Lecture Notes in Computer Science, 2012
This paper shows a concrete example of the use of graph mining for tracking objects in videos with moving cameras and without any contextual information on the objects to track. To make the mining algorithm efficient, we benefit from a video representation based on dynamic (evolving through time) planar graphs. We then define a number of constraints to efficiently find our so-called spatio-temporal graph patterns. Those patterns are linked through an occurrences graph to allow us to tackle occlusion or graph features instability problems in the video. Experiments on synthetic and real videos show that our method is effective and allows us to find relevant patterns for our tracking application.
Video tracking based on sequential particle filtering on graphs
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2011
In this paper, we develop a novel solution for particle filtering on general graphs. We provide an exact solution for particle filtering on directed cycle-free graphs. The proposed approach relies on a partial-order relation in an antichain decomposition that forms a high-order Markov chain over the partitioned graph. We subsequently derive a closed-form sequential updating scheme for conditional density propagation using particle filtering on directed cycle-free graphs. We also provide an approximate solution for particle filtering on general graphs by splitting graphs with cycles into multiple directed cycle-free subgraphs. We then use the sequential updating scheme by alternating among the directed cycle-free subgraphs to obtain an estimate of the density propagation. We rely on the proposed method for particle filtering on general graphs for two video tracking applications: 1) object tracking using high-order Markov chains; and 2) distributed multiple object tracking based on multi-object graphical interaction models. Experimental results demonstrate the improved performance of the proposed approach to particle filtering on graphs compared with existing methods for video tracking.
Graph Moving Object Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020
Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to undesirable variations in the background scene, MOS becomes very challenging for static and moving camera sequences. Several deep learning methods have been proposed for MOS with impressive performance. However, these methods show performance degradation in the presence of unseen videos; and usually, deep learning models require large amounts of data to avoid overfitting. Recently, graph learning has attracted significant attention in many computer vision applications since they provide tools to exploit the geometrical structure of data. In this work, concepts of graph signal processing are introduced for MOS. First, we propose a new algorithm that is composed of segmentation, background initialization, graph construction, unseen sampling, and a semi-supervised learning method inspired by the theory of recovery of graph signals. Secondly, theoretical developments are introduced, showing one bound for the sample complexity in semi-supervised learning, and two bounds for the condition number of the Sobolev norm. Our algorithm has the advantage of requiring less labeled data than deep learning methods while having competitive results on both static and moving camera videos. Our algorithm is also adapted for Video Object Segmentation (VOS) tasks and is evaluated on six publicly available datasets outperforming several state-of-the-art methods in challenging conditions.