Adaptive uncertainty estimation for particle filter-based trackers (original) (raw)
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Improving the robustness of particle filter-based visual trackers using online parameter adaptation
2007 IEEE Conference on Advanced Video and Signal Based Surveillance, 2007
In particle filter-based visual trackers, dynamic velocity components are typically incorporated into the state update equations. In these cases, there is a risk that the uncertainty in the model update stage can become amplified in unexpected and undesirable ways, leading to erroneous behavior of the tracker. Moreover, the use of a weak appearance model can make the estimates provided by the particle filter inaccurate. To deal with this problem, we propose a continuously adaptive approach to estimating uncertainty in the particle filter, one that balances the uncertainty in its static and dynamic elements. We provide quantitative performance evaluation of the resulting particle filter tracker on a set of ten video sequences. Results are reported in terms of a metric that can be used to objectively evaluate the performance of visual trackers. This metric is used to compare our modified particle filter tracker and the continuously adaptive mean shift tracker. Results show that the performance of the particle filter is significantly improved through adaptive parameter estimation, particularly in cases of occlusion and erratic, nonlinear target motion.
Particle filter–based visual tracking with a first order dynamic model and uncertainty adaptation
In many real world applications, tracking must be performed reliably in real-time for sufficiently long periods where target appearance and motion may sensibly change from one frame to the following. In such non ideal conditions this is likely to determine inaccurate estimates of the target location unless dynamic components are incorporated in the model. To deal with these problems effectively, we propose a particle filter-based tracker that exploits a a first order dynamic model and continuously performs adaptation of model noise so to balance uncertainty between the static and dynamic components of the state vector. We provide an extensive set of experimental evidences with a comparative performance analysis with tracking methods representative of the principal approaches. Results show that the method proposed is particularly effective for real-time tracking over long video sequences with occlusions and erratic, non-linear target motion.
Combination of videobased camera trackers using a dynamically adapted particle filter
2007
Abstract: This paper presents a video-based camera tracker that combines marker-based and feature point-based cues in a particle filter framework. The framework relies on their complementary performance. Marker-based trackers can robustly recover camera position and orientation when a reference (marker) is available, but fail once the reference becomes unavailable. On the other hand, feature point tracking can still provide estimates given a limited number of feature points.
A Self-adaptive Likelihood Function for Tracking with Particle Filter
Proceedings of the 10th International Conference on Computer Vision Theory and Applications, 2015
The particle filter is known to be efficient for visual tracking. However, its parameters are empirically fixed, depending on the target application, the video sequences and the context. In this paper, we introduce a new algorithm which automatically adjusts online two majors of them: the correction and the propagation parameters. Our purpose is to determine, for each frame of a video, the optimal value of the correction parameter and to adjust the propagation one to improve the tracking performance. On one hand, our experimental results show that the common settings of particle filter are sub-optimal. On another hand, we prove that our approach achieves a lower tracking error without needing to tune these parameters. Our adaptive method allows to track objects in complex conditions (illumination changes, cluttered background, etc.) without adding any computational cost compared to the common usage with fixed parameters.
Augmented particle filtering for efficient visual tracking
IEEE International Conference on Image Processing 2005, 2005
Visual tracking is one of the key tasks in computer vision. The particle filter algorithm has been extensively used to tackle this problem due to its flexibility. However the conventional particle filter uses system transition as the proposal distribution, frequently resulting in poor priors for the filtering step. The main reason is that it is difficult, if not impossible, to accurately model the target's motion. Such a proposal distribution does not take into account the current observations. It is not a trivial task to devise a satisfactory proposal distribution for the particle filter. In this paper we advance a general augmented particle filtering framework for designing the optimal proposal distribution. The essential idea is to augment a second filter's estimate into the proposal distribution design. We then show that several existing improved particle filters can be rationalised within this general framework. Based on this framework we further propose variant algorithms for robust and efficient visual tracking. Experiments indicate that the augmented particle filters are more efficient and robust than the conventional particle filter.
Correlation Particle Filter for Visual Tracking
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2018
In this paper, we propose a novel correlation particle filter (CPF) for robust visual tracking. Instead of a simple combination of a correlation filter and a particle filter, we exploit and complement the strength of each one. Compared with existing tracking methods based on correlation filters and particle filters, the proposed tracker has four major advantages: 1) it is robust to partial and total occlusions, and can recover from lost tracks by maintaining multiple hypotheses; 2) it can effectively handle large-scale variation via a particle sampling strategy; 3) it can efficiently maintain multiple modes in the posterior density using fewer particles than conventional particle filters, resulting in low computational cost; and 4) it can shepherd the sampled particles toward the modes of the target state distribution using a mixture of correlation filters, resulting in robust tracking performance. Extensive experimental results on challenging benchmark data sets demonstrate that th...
Object Tracking in Video Using Particle Filtering
2006
Object tracking in video is an important problem which has many applications like video surveillance, target tracking using video sensors, etc. This paper presents an approach to object tracking in a video sequence using particle filtering. The motion edge is modelled using a six parameter model. Spatio-temporal filtering techniques are used to determine the velocity of the moving edge. Particle filtering is used to propagate the multi dimensional posterior density over time. Experimental results have been presented to show the effectiveness of the proposed method.
Effects of Parameters Variations in Particle Filter Tracking
2006 International Conference on Image Processing, 2006
Many implementations of visual tracking have been proposed since many years. The lack of standard evaluation process has prevented fair comparison between them. In this paper, we simply propose to evaluate different particle filter methods in people tracking applications. We introduce an objective metric and give results according to different parameter variations. Finally, based on our evaluations, we can propose a new particle filter configuration that outperforms other current implementations.
2015
Occlusions, one of the most challenging problems in visual tracking, degrade the performance of many trackers significantly. Taking various spatial and temporal forms, occlusions have not been modeled completely yet. State-of-the-art solutions fail to handle persistent and complex occlusions, and mostly address partial or temporal occlusions. Additionally, the solutions around these problems are not unified, and researchers limit their solutions to a tiny portion of the problem. Despite the large number of studies of handling occlusion, only a few of them have actually studied the occlusion phenomenon itself and devised solutions for occlusion detection and reasoning. Any comprehensive study over different approaches of occlusion handling is deemed missing. To address this shortcoming, this study first presents a comprehensive review on the literature. The occlusion problem is defined, its challenges are described, and several research directions to handle it are distinguished. Next...
An Adaptive Particle Filtering for Solving Occlusion Problems of Video Tracking
Communications in Computer and Information Science, 2015
In recent years, the visual object tracking has drawn increasing interests. There are many applications, e.g., video surveillance in airports, schools, hospitals and traffic. The object surveillance may provide crucial information about the behavior, interaction, and relationship between objects of interest. This paper addresses issues in object tracking where videos contain complex scenarios. We propose an adaptive particle filters tracking scheme with exquisite resampling (AERPF), which improves prediction, importance sampling and resampling. In prediction step, an adaptive strategy for search region and particle number is addressed for object disappearing or obstacle disturbance, which can obtain results more effectively. In addition, in importance sampling, we use optical flow to refine the particle weights using the dynamical object motion information, which results the better accuracy of object location updating. Moreover, exquisite resampling (ER) algorithm can be applied for reflecting more the posterior probability density function of true state. The proposed method can be applied for object tracking both on fixed and active camera, handling partial occlusion and full occlusion problem properly. As a result, it outperforms other existing methods.