Behavioral Recognition and multi-target tracking in partially observed environments

Arsene Fansi Tchango 1, 2, 3
1 MAIA - Autonomous intelligent machine
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : In this thesis, we are interested in the problem of pedestrian behavioral tracking within a critical environment partially under sensory coverage. While most of the works found in the literature usually focus only on either the location of a pedestrian or the activity a pedestrian is undertaking, we stands in a general view and consider estimating both data simultaneously. The contributions presented in this document are organized in two parts. The first part focuses on the representation and the exploitation of the environmental context for serving the purpose of behavioral estimation. The state of the art shows few studies addressing this issue where graphical models with limited expressiveness capacity such as dynamic Bayesian networks are used for modeling prior environmental knowledge. We propose, instead, to rely on richer contextual models issued from autonomous agent-based behavioral simulators and we demonstrate the effectiveness of our approach through extensive experimental evaluations. The second part of the thesis addresses the general problem of pedestrians’ mutual influences, commonly known as targets’ interactions, on their respective behaviors during the tracking process. Under the assumption of the availability of a generic simulator (or a function) modeling the tracked targets' behaviors, we develop a yet scalable approach in which interactions are considered at low computational cost. The originality of the proposed approach resides on the introduction of density-based aggregated information, called ‘’representatives’’, computed in such a way to guarantee the behavioral diversity for each target, and on which the filtering system relies for computing, in a finer way, behavioral estimations even in case of occlusions. We present the modeling choices, the resulting algorithms as well as a set of challenging scenarios on which the proposed approach is evaluated.
Complete list of metadatas

Cited literature [208 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/tel-01251204
Contributor : Arsene Fansi Tchango <>
Submitted on : Thursday, January 7, 2016 - 2:26:49 PM
Last modification on : Tuesday, December 18, 2018 - 4:40:22 PM
Long-term archiving on : Friday, April 8, 2016 - 1:07:17 PM

Identifiers

  • HAL Id : tel-01251204, version 2

Citation

Arsene Fansi Tchango. Behavioral Recognition and multi-target tracking in partially observed environments. Computer Science [cs]. Université de Lorraine, 2015. English. ⟨tel-01251204v2⟩

Share

Metrics

Record views

331

Files downloads

796