People tracking with human motion predictions from social forces

Luber, Matthias; Stork, Johannes A.; Tipaldi, Gian Diego; Arras, Kai O.

doi:10.1109/robot.2010.5509779

Cited by 271 publications

(189 citation statements)

References 12 publications

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“…Its motion equations are derived from Newtons law F = ma. The forces a character is driven by are substantially of three kinds (Luber et al 2010). An attractive motivational force F mot pulls characters toward some scheduled destination, while repulsive physical forces F phy and interaction forces F int prevent from collision into physical objects and take into account interactions within characters.…”

Section: Resultsmentioning

confidence: 99%

“…A human operator interacts with the crowd by opening doors to let it flow, while trying to minimize the time a door remains open. Although somehow simplified with respect to more complex works, such as (Luber et al 2010) (where additional assumptions on trajectories' regularity are made), the developed model results in a good overall output, where people behave correctly and realistically.…”

Section: Resultsmentioning

confidence: 99%

“…At last, the model employed in simulation combines technical and social aspects following the current trend in literature. As shown for instance in (Mehran et al 2009;Pellegrini et al 2009;Luber et al 2010;Mazzon et al 2013), a social force model describing interactions among the individual members of a group of people has been proposed to detect abnormal events in crowd videos. Here people are treated as interacting particles subject to internal and external physical forces which determine their motion and global behaviour.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bio-inspired relevant interaction modelling in cognitive crowd management

Chiappino

Morerio

Regazzoni

2014

J Ambient Intell Human Comput

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Cognitive algorithms, integrated in intelligent systems, represent an important innovation in designing interactive smart environments. More in details, Cognitive Systems have important applications in anomaly detection and management in advanced video surveillance. These algorithms mainly address the problem of modelling interactions and behaviours among the main entities in a scene. A bio-inspired structure is here proposed, which is able to encode and synthesize signals, not only for the description of single entities behaviours, but also for modelling cause-effect relationships between user actions and changes in environment configurations. Such models are stored within a memory (Autobiographical Memory) during a learning phase. Here the system operates an effective knowledge transfer from a human operator towards an automatic systems called Cognitive Surveillance Node (CSN), which is part of a complex cognitive JDL-based and bio-inspired architecture. After such a knowledge-transfer phase, learned representations can be used, at different levels, either to support human decisions, by detecting anomalous interaction models and thus compensating for human shortcomings, or, in an automatic decision scenario, to identify anomalous patterns and choose the best strategy to preserve stability of the entire system. Results are presented in a video surveillance scenario, where the CSN can observe two interacting entities consisting in a simulated crowd and a human operator.These can interact within a visual 3D simulator, where crowd behaviour is modelled by means of Social Forces. The way anomalies are detected and consequently handled is demonstrated, on synthetic and also on real video sequences, in both the user-support and automatic modes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bio-inspired relevant interaction modelling in cognitive crowd management

Chiappino

Morerio

Regazzoni

2014

J Ambient Intell Human Comput

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“…These have been defined in what is called the Social Force Model (SFM) [21], which has been used for abnormal crowd behavior detection [22], crowd simulation [23] and has only recently been applied to multiple people tracking: in [24], an energy minimization approach is used to predict the future position of each pedestrian considering all the terms of the social force model. In [10] and [25], the social forces are included in the motion model of the Kalman or Extended Kalman filter. In [26] a method is presented to detect small groups of people in a crowd, but it is only recently that grouping behavior has been included in a tracking framework [11,27,28].…”

Section: Related Workmentioning

confidence: 99%

Exploiting Pedestrian Interaction via Global Optimization and Social Behaviors

Leal-Taixé

Pons-Moll

Rosenhahn

2012

Lecture Notes in Computer Science

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Abstract. Multiple people tracking consists in detecting the subjects at each frame and matching these detections to obtain full trajectories. In semi-crowded environments, pedestrians often occlude each other, making tracking a challenging task. Tracking methods mostly work with the assumption that each pedestrian moves independently unaware of the objects or the other pedestrians around it. In the real world though, it is clear that when walking in a crowd, pedestrians try to avoid collisions, keep a close distance to a group of friends or avoid static obstacles in the scene. In this paper, we present an approach which includes the interaction between pedestrians in two ways: first, including social and grouping behavior as a physical model within the tracking system, and second, using a global optimization scheme which takes into account all trajectories and all frames to solve the data association problem . Results are presented on three challenging publicly available datasets, showing our method outperforms state-of-the-art tracking systems. We also make a thorough analysis of the effect of the parameters of the proposed tracker as well as its robustness against noise, outliers and missing data.

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“…The linear trajectory avoidance model [15] used the repulsion effect among pedestrians to predict local motion paths for individual pedestrians. Luber et al [12] extended the use of repulsion effect to include scene obstacles to improve motion prediction. Choi et al [5] considered both repulsion effects and group motion dynamics within a joint prediction model.…”

Section: Introductionmentioning

confidence: 99%

Predicting Social Interactions for Visual Tracking

Kakadiaris

Shah

2011

Procedings of the British Machine Vision Conference 2011

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Human interaction dynamics are known to play an important role in the development of robust pedestrian trackers that are applicable to a variety of applications in video surveillance. Traditional approaches to pedestrian tracking assume that each pedestrian walks independently and the tracker predicts the location based on an underlying motion model, such as a constant velocity or autoregressive model. Recent approaches have begun to leverage interaction, especially by modeling the repulsion force, among pedestrians to improve motion predictions. However, human interaction is more complex and is influenced by both repulsion and attraction effects. This motivates the use of a more complex human interaction model for pedestrian tracking. In this paper, we propose a novel visual tracking method by leveraging complex social interactions. We present an algorithm that decomposes social interactions into multiple potential interaction modes. We integrate these multiple social interaction modes into an interactive Markov Chain Monte Carlo tracker. We demonstrate how the developed method translates into a more informed motion prediction, resulting in a robust tracking performance. We test our method on videos from unconstrained outdoor environments and compare it against popular multi-object trackers.

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People tracking with human motion predictions from social forces

Cited by 271 publications

References 12 publications

Bio-inspired relevant interaction modelling in cognitive crowd management

Bio-inspired relevant interaction modelling in cognitive crowd management

Exploiting Pedestrian Interaction via Global Optimization and Social Behaviors

Predicting Social Interactions for Visual Tracking

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