17,913 research outputs found
Robust Mobile Object Tracking Based on Multiple Feature Similarity and Trajectory Filtering
This paper presents a new algorithm to track mobile objects in different
scene conditions. The main idea of the proposed tracker includes estimation,
multi-features similarity measures and trajectory filtering. A feature set
(distance, area, shape ratio, color histogram) is defined for each tracked
object to search for the best matching object. Its best matching object and its
state estimated by the Kalman filter are combined to update position and size
of the tracked object. However, the mobile object trajectories are usually
fragmented because of occlusions and misdetections. Therefore, we also propose
a trajectory filtering, named global tracker, aims at removing the noisy
trajectories and fusing the fragmented trajectories belonging to a same mobile
object. The method has been tested with five videos of different scene
conditions. Three of them are provided by the ETISEO benchmarking project
(http://www-sop.inria.fr/orion/ETISEO) in which the proposed tracker
performance has been compared with other seven tracking algorithms. The
advantages of our approach over the existing state of the art ones are: (i) no
prior knowledge information is required (e.g. no calibration and no contextual
models are needed), (ii) the tracker is more reliable by combining multiple
feature similarities, (iii) the tracker can perform in different scene
conditions: single/several mobile objects, weak/strong illumination,
indoor/outdoor scenes, (iv) a trajectory filtering is defined and applied to
improve the tracker performance, (v) the tracker performance outperforms many
algorithms of the state of the art
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 201
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
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