Distributed Multi-Target Tracking in Camera Networks

Casao, Sara; García-Bosque, M.; Murillo, Ana C.; Montijano, Eduardo

doi:10.1109/icra48506.2021.9562055

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…Algorithm 1 Implicit Control and Dynamic Assignment Strategy at time t 1: X (t), u(t) ← obtain herders' and evaders' positions at instant t 2: It is noteworthy that we are implicitly assuming that the dynamics of the evaders are perfectly known, and that the position of all the entities can be observed. Our proposal can be directly extended to adaptive scenarios with uncertainty in the motion of the evaders [17], robust frameworks that recognizes the evaders [4] or/and a communication-aware setups by means of, e.g., a distributed Kalman filter [16].…”

Section: Proposed Solutionmentioning

confidence: 99%

Multi-robot Implicit Control of Herds

Sebastián

Montijano

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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This paper solves the problem of herding countless evaders by means of a few robots. The objective is to steer all the evaders towards a desired tracking reference while avoiding escapes. The problem is very challenging due to the highly complex repulsive evaders' dynamics and the underdetermined states to control. We propose a solution that is based on Implicit Control and a novel dynamic assignment strategy to select the evaders to be directly controlled. The former is a general technique that explicitly computes control inputs even in highly complex input-nonaffine dynamics. The latter is built upon a convex-hull dynamic clustering inspired by the Voronoi tessellation problem. The combination of both allows to choose the best evaders to directly control, while the others are indirectly controlled by exploiting the repulsive interactions among them. Simulations show that massive herds can be herd throughout complex patterns by means of a few herders.

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Section: Proposed Solutionmentioning

confidence: 99%

Multi-robot Implicit Control of Herds

Sebastián

Montijano

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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“…Multiple problems addressed in robotics, such as tracking, navigation or mapping, entail the presence of people in their real-world applications [1]- [4]. Performing a systematic evaluation of such methods in environments with humans is a major problem.…”

Section: Introductionmentioning

confidence: 99%

A Self-Adaptive Gallery Construction Method for Open-World Person Re-Identification

Casao

Azagra

Murillo

et al. 2023

Sensors

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Person re-identification, or simply re-id, is the task of identifying again a person who has been seen in the past by a perception system. Multiple robotic applications, such as tracking or navigate-and-seek, use re-identification systems to perform their tasks. To solve the re-id problem, a common practice consists in using a gallery with relevant information about the people already observed. The construction of this gallery is a costly process, typically performed offline and only once because of the problems associated with labeling and storing new data as they arrive in the system. The resulting galleries from this process are static and do not acquire new knowledge from the scene, which is a limitation of the current re-id systems to work for open-world applications. Different from previous work, we overcome this limitation by presenting an unsupervised approach to automatically identify new people and incrementally build a gallery for open-world re-id that adapts prior knowledge with new information on a continuous basis. Our approach performs a comparison between the current person models and new unlabeled data to dynamically expand the gallery with new identities. We process the incoming information to maintain a small representative model of each person by exploiting concepts of information theory. The uncertainty and diversity of the new samples are analyzed to define which ones should be incorporated into the gallery. Experimental evaluation in challenging benchmarks includes an ablation study of the proposed framework, the assessment of different data selection algorithms that demonstrate the benefits of our approach, and a comparative analysis of the obtained results with other unsupervised and semi-supervised re-id methods.

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