18,829 research outputs found
Accurate video object tracking using a region-based particle filter
Usually, in particle filters applied to video tracking, a simple geometrical shape, typically an ellipse, is used in order to bound the object being tracked. Although it is a good tracker, it tends to a bad object representation, as most of the world objects are not simple geometrical shapes. A better way to represent the object is by using a region-based approach, such as the Region Based Particle Filter (RBPF). This method exploits a hierarchical region based representation associated with images to tackle both problems at the same time: tracking and video object segmentation. By means of RBPF the object segmentation is resolved with high accuracy, but new problems arise. The object representation is now based on image partitions instead of pixels. This means that the amount of possible combinations has now decreased, which is computationally good, but an error on the regions taken for the object representation leads to a higher estimation error than methods working at pixel level. On the other hand, if the level of regions detail in the partition is high, the estimation of the object turns to be very noisy, making it hard to accurately propagate the object segmentation. In this thesis we present new tools to the existing RBPF. These tools are focused on increasing the RBPF performance by means of guiding the particles towards a good solution while maintaining a particle filter approach. The concept of hierarchical flow is presented and exploited, a Bayesian estimation is used in order to assign probabilities of being object or background to each region, and the reduction, in an intelligent way, of the solution space , to increase the RBPF robustness while reducing computational effort. Also changes on the already proposed co-clustering in the RBPF approach are proposed. Finally, we present results on the recently presented DAVIS database. This database comprises 50 High Definition video sequences representing several challenging situations. By using this dataset, we compare the RBPF with other state-ofthe- art methods
A novel object tracking algorithm based on compressed sensing and entropy of information
Acknowledgments This research is supported by (1) the Ph.D. Programs Foundation of Ministry of Education of China under Grant no. 20120061110045, (2) the Science and Technology Development Projects of Jilin Province of China under Grant no. 20150204007G X, and (3) the Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China.Peer reviewedPublisher PD
Realtime Multilevel Crowd Tracking using Reciprocal Velocity Obstacles
We present a novel, realtime algorithm to compute the trajectory of each
pedestrian in moderately dense crowd scenes. Our formulation is based on an
adaptive particle filtering scheme that uses a multi-agent motion model based
on velocity-obstacles, and takes into account local interactions as well as
physical and personal constraints of each pedestrian. Our method dynamically
changes the number of particles allocated to each pedestrian based on different
confidence metrics. Additionally, we use a new high-definition crowd video
dataset, which is used to evaluate the performance of different pedestrian
tracking algorithms. This dataset consists of videos of indoor and outdoor
scenes, recorded at different locations with 30-80 pedestrians. We highlight
the performance benefits of our algorithm over prior techniques using this
dataset. In practice, our algorithm can compute trajectories of tens of
pedestrians on a multi-core desktop CPU at interactive rates (27-30 frames per
second). To the best of our knowledge, our approach is 4-5 times faster than
prior methods, which provide similar accuracy
A Deep-structured Conditional Random Field Model for Object Silhouette Tracking
In this work, we introduce a deep-structured conditional random field
(DS-CRF) model for the purpose of state-based object silhouette tracking. The
proposed DS-CRF model consists of a series of state layers, where each state
layer spatially characterizes the object silhouette at a particular point in
time. The interactions between adjacent state layers are established by
inter-layer connectivity dynamically determined based on inter-frame optical
flow. By incorporate both spatial and temporal context in a dynamic fashion
within such a deep-structured probabilistic graphical model, the proposed
DS-CRF model allows us to develop a framework that can accurately and
efficiently track object silhouettes that can change greatly over time, as well
as under different situations such as occlusion and multiple targets within the
scene. Experiment results using video surveillance datasets containing
different scenarios such as occlusion and multiple targets showed that the
proposed DS-CRF approach provides strong object silhouette tracking performance
when compared to baseline methods such as mean-shift tracking, as well as
state-of-the-art methods such as context tracking and boosted particle
filtering.Comment: 17 page
Physical simulation for monocular 3D model based tracking
The problem of model-based object tracking in three dimensions is addressed. Most previous work on tracking assumes simple motion models, and consequently tracking typically fails in a variety of situations. Our insight is that incorporating physics models of object behaviour improves tracking performance in these cases. In particular it allows us to handle tracking in the face of rigid body interactions where there is also occlusion and fast object motion. We show how to incorporate rigid body physics simulation into a particle filter. We present two methods for this based on pose and force noise. The improvements are tested on four videos of a robot pushing an object, and results indicate that our approach performs considerably better than a plain particle filter tracker, with the force noise method producing the best results over the range of test videos
Understanding and Diagnosing Visual Tracking Systems
Several benchmark datasets for visual tracking research have been proposed in
recent years. Despite their usefulness, whether they are sufficient for
understanding and diagnosing the strengths and weaknesses of different trackers
remains questionable. To address this issue, we propose a framework by breaking
a tracker down into five constituent parts, namely, motion model, feature
extractor, observation model, model updater, and ensemble post-processor. We
then conduct ablative experiments on each component to study how it affects the
overall result. Surprisingly, our findings are discrepant with some common
beliefs in the visual tracking research community. We find that the feature
extractor plays the most important role in a tracker. On the other hand,
although the observation model is the focus of many studies, we find that it
often brings no significant improvement. Moreover, the motion model and model
updater contain many details that could affect the result. Also, the ensemble
post-processor can improve the result substantially when the constituent
trackers have high diversity. Based on our findings, we put together some very
elementary building blocks to give a basic tracker which is competitive in
performance to the state-of-the-art trackers. We believe our framework can
provide a solid baseline when conducting controlled experiments for visual
tracking research
- …