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49
Segmentation and tracking of multiple humans in crowded environments
- IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Tracking of humans in dynamic scenes has been an important topic of research. Most techniques, however, are limited to situations where humans appear isolated and occlusion is small. Typical methods rely on appearance models that must be acquired when the humans enter the scene and are not occluded. ..."
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Cited by 171 (5 self)
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Tracking of humans in dynamic scenes has been an important topic of research. Most techniques, however, are limited to situations where humans appear isolated and occlusion is small. Typical methods rely on appearance models that must be acquired when the humans enter the scene and are not occluded. We present a method that can track humans in crowded environments, with significant and persistent occlusion by making use of human shape models in addition to camera models, the assumption that humans walk on a plane and acquired appearance models. Experimental results and a quantitative evaluation are included. 1
Tracking multiple humans in complex situations
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2004
"... Abstract—Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. In the first part, we show how multiple human objects are ..."
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Cited by 134 (3 self)
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Abstract—Tracking multiple humans in complex situations is challenging. The difficulties are tackled with appropriate knowledge in the form of various models in our approach. Human motion is decomposed into its global motion and limb motion. In the first part, we show how multiple human objects are segmented and their global motions are tracked in 3D using ellipsoid human shape models. Experiments show that it successfully applies to the cases where a small number of people move together, have occlusion, and cast shadow or reflection. In the second part, we estimate the modes (e.g., walking, running, standing) of the locomotion and 3D body postures by making inference in a prior locomotion model. Camera model and ground plane assumptions provide geometric constraints in both parts. Robust results are shown on some difficult sequences. Index Terms—Multiple-human segmentation, multiple-human tracking, visual surveillance, human shape model, human locomotion model. 1
A distributed algorithm for managing multi-target identities in wireless ad-hoc sensor networks
- In IPSN ’03: Information Processing in Sensor Networks
, 2003
"... Abstract. This paper presents a scalable distributed algorithm for computing and maintaining multi-target identity information. The algorithm builds on a novel representational framework, Identity-Mass Flow, to overcome the problem of exponential computational complexity in managing multi-target ide ..."
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Cited by 69 (12 self)
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Abstract. This paper presents a scalable distributed algorithm for computing and maintaining multi-target identity information. The algorithm builds on a novel representational framework, Identity-Mass Flow, to overcome the problem of exponential computational complexity in managing multi-target identity explicitly. The algorithm uses local information to efficiently update the global multi-target identity information represented as a doubly stochastic matrix, and can be efficiently mapped to nodes in a wireless ad hoc sensor network. The paper describes a distributed implementation of the algorithm in sensor networks. Simulation results have validated the Identity-Mass Flow framework and demonstrated the feasibility of the algorithm. 1
Segmentation and Tracking of Multiple Humans in Crowded Environments
"... Abstract—Segmentation and tracking of multiple humans in crowded situations is made difficult by interobject occlusion. We propose a model-based approach to interpret the image observations by multiple partially occluded human hypotheses in a Bayesian framework. We define a joint image likelihood fo ..."
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Cited by 53 (0 self)
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Abstract—Segmentation and tracking of multiple humans in crowded situations is made difficult by interobject occlusion. We propose a model-based approach to interpret the image observations by multiple partially occluded human hypotheses in a Bayesian framework. We define a joint image likelihood for multiple humans based on the appearance of the humans, the visibility of the body obtained by occlusion reasoning, and foreground/background separation. The optimal solution is obtained by using an efficient sampling method, data-driven Markov chain Monte Carlo (DDMCMC), which uses image observations for proposal probabilities. Knowledge of various aspects, including human shape, camera model, and image cues, are integrated in one theoretically sound framework. We present experimental results and quantitative evaluation, demonstrating that the resulting approach is effective for very challenging data. Index Terms—Multiple human segmentation, multiple human tracking, Markov chain Monte Carlo. Ç 1
Multiple person and speaker activity tracking with a particle filter,” in
- Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing,
, 2004
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Tracking deforming objects using particle filtering for geometric active contours
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2007
"... Tracking deforming objects involves estimating the global motion of the object and its local deformations as a function of time. Tracking algorithms using Kalman filters or particle filters have been proposed for finite dimensional representations of shape, but these are dependent on the chosen par ..."
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Cited by 41 (7 self)
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Tracking deforming objects involves estimating the global motion of the object and its local deformations as a function of time. Tracking algorithms using Kalman filters or particle filters have been proposed for finite dimensional representations of shape, but these are dependent on the chosen parametrization and cannot handle changes in curve topology. Geometric active contours provide a framework which is parametrization independent and allow for changes in topology. In the present work, we formulate a particle filtering algorithm in the geometric active contour framework that can be used for tracking moving and deforming objects. To the best of our knowledge, this is the first attempt to implement an approximate particle filtering algorithm for tracking on a (theoretically) infinite dimensional state space.
A probabilistic framework for multi-modal multi-person tracking
- In IEEE Workshop on Multi-Object Tracking
, 2003
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Multiple hypothesis tracking of clusters of people
- in Proc. IROS
, 2006
"... Abstract—Mobile robots operating in populated environments typically can improve their service and navigation behavior when they know where people are in their vicinity and in which direction they are heading. In this paper we present an algorithm for tracking clusters of people using Multiple Hypot ..."
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Cited by 17 (5 self)
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Abstract—Mobile robots operating in populated environments typically can improve their service and navigation behavior when they know where people are in their vicinity and in which direction they are heading. In this paper we present an algorithm for tracking clusters of people using Multiple Hypothesis Tracking (MHT). The motivation for our approach is that tracking clusters of objects instead of the individual objects enhances the reliability and robustness of the tracking especially when the objects move in groups. To efficiently keep track of multiple objects and clusters, our approach uses MHT in combination with Murty’s algorithm. The set of hypothesis for each iteration is constructed in two consecutive steps: one for solving the data association problem, taking also into account the frequent occlusions between the objects, and the second one for considering the joining of different clusters. Our approach has been implemented and tested on a real robot and in a typical hallway environment. Experimental results demonstrate that our approach can robustly deal with several groups of people and is able to reliably manage the splits and joins of clusters. I.
Multi-level Particle Filter Fusion of Features and Cues for Audio-Visual Person Tracking
"... Abstract. In this paper, two multimodal systems for the tracking of multiple users in smart environments are presented. The first is a multiview particle filter tracker using foreground, color and special upper body detection and person region features. The other is a wide angle overhead view person ..."
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Cited by 13 (5 self)
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Abstract. In this paper, two multimodal systems for the tracking of multiple users in smart environments are presented. The first is a multiview particle filter tracker using foreground, color and special upper body detection and person region features. The other is a wide angle overhead view person tracker relying on foreground segmentation and model-based blob tracking. Both systems are completed by a joint probabilistic data association filter-based source localizer using the input from several microphone arrays. While the first system fuses audio and visual cues at the feature level, the second one incorporates them at the decision level using state-based heuristics. The systems are designed to estimate the 3D scene locations of room occupants and are evaluated based on their precision in estimating person locations, their accuracy in recognizing person configurations and their ability to consistently keep track identities over time. The trackers are extensively tested and compared, for each separate
Observe-and-explain: A new approach for multiple hypotheses tracking of humans and objects
- In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR
, 2008
"... Abstract This paper presents a novel approach for tracking humans and objects under severe occlusion. We introduce a new paradigm for multiple hypotheses tracking, observe-and-explain, as opposed to the previous paradigm of hypothesize-and-test. Our approach efficiently enumerates multiple possibil ..."
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Cited by 11 (3 self)
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Abstract This paper presents a novel approach for tracking humans and objects under severe occlusion. We introduce a new paradigm for multiple hypotheses tracking, observe-and-explain, as opposed to the previous paradigm of hypothesize-and-test. Our approach efficiently enumerates multiple possibilities of tracking by generating several likely 'explanations' after concatenating a sufficient amount of observations. The computational advantages of our approach over the previous paradigm under severe occlusions are presented. The tracking system is implemented and tested using the i-Lids dataset, which consists of videos of humans and objects moving in a London subway station. The experimental results show that our new approach is able to track humans and objects accurately and reliably even when they are completely occluded, illustrating its advantage over previous approaches.
