Evaluation of on-line quality estimators for object tracking

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. C. SanMiguel, A. Cavallaro, and J. M. Martínez, "Evaluation of on-line quality estimators for object tracking", in 17th IEEE International Conference on Image Processing, ICIP 2010, p. 825-828Failure of tracking algorithms is inevitable in real and on-line tracking systems. The online estimation of the track quality is therefore desirable for detecting tracking failures while the algorithm is operating. In this paper, we propose a taxonomy and present a comparative evaluation of online quality estimators for video object tracking. The measures are compared over a heterogeneous video dataset with standard sequences. Among other results, the experiments show, that the Observation Likelihood (OL) measure is an appropriate quality measure for overall tracking performance evaluation, while the Template Inverse Matching (TIM) measure is appropriate to detect the start and the end instants of tracking failures.Work partially supported by the Spanish Government (TEC2007- 65400 SemanticVideo), Cátedra Infoglobal-UAM for “Nuevas Tecnologías de video aplicadas a la seguridad”, Consejería de Educación of the Comunidad de Madrid and European Social Fund. Part of the work reported in this paper was done during a research stay of the first author under a research grant (funded by UAM) at Queen Mary University of London (UK)

Coherent Selection of Independent Trackers for Real-time Object Tracking

International audienceThis paper presents a new method for combining several independent and heterogeneous tracking algorithms for the task of online single-object tracking. The proposed algorithm runs several trackers in parallel, where each of them relies on a different set of complementary low-level features. Only one tracker is selected at a given frame, and the choice is based on a spatio-temporal coherence criterion and normalised confidence estimates. The key idea is that the individual trackers are kept completely independent, which reduces the risk of drift in situations where for example a tracker with an inaccurate or inappropriate appearance model negatively impacts the performance of the others. Moreover, the proposed approach is able to switch between different tracking methods when the scene conditions or the object appearance rapidly change. We experimentally show with a set of Online Adaboost-based trackers that this formulation of multiple trackers improves the tracking results in comparison to more classical combinations of trackers. And we further improve the overall performance and computational efficiency by introducing a selective update step in the tracking framework

Dynamic Partitioned Sampling For Tracking With Discriminative Features

We present a multi-cue fusion method for tracking with particle filters which relies on a novel hierarchical sampling strategy. Similarly to previous works, it tackles the problem of tracking in a relatively high-dimensional state space by dividing such a space into partitions, each one corresponding to a single cue, and sampling from them in a hierarchical manner. However, unlike other approaches, the order of partitions is not fixed a priori but changes dynamically depending on the reliability of each cue, i.e. more reliable cues are sampled first. We call this approach Dynamic Partitioned Sampling (DPS). The reliability of each cue is measured in terms of its ability to discriminate the object with respect to the background, where the background is not described by a fixed model or by random patches but is represented by a set of informative "background particles" which are tracked in order to be as similar as possible to the object. The effectiveness of this general framework is demonstrated on the specific problem of head tracking with three different cues: colour, edge and contours. Experimental results prove the robustness of our algorithm in several challenging video sequences

Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking

Given an object model and a black-box measure of similarity between the model and candidate targets, we consider visual object tracking as a numerical optimization problem. During normal tracking conditions when the object is visible from frame to frame, local optimization is used to track the local mode of the similarity measure in a parameter space of translation, rotation and scale. However, when the object becomes partially or totally occluded, such local tracking is prone to failure, especially when common prediction techniques like the Kalman filter do not provide a good estimate of object parameters in future frames. To recover from these inevitable tracking failures, we consider object detection as a global optimization problem and solve it via Adaptive Simulated Annealing (ASA), a method that avoids becoming trapped at local modes and is much faster than exhaustive search. As a Monte Carlo approach, ASA stochastically samples the parameter space, in contrast to local deterministic search. We apply cluster analysis on the sampled parameter space to redetect the object and renew the local tracker. Our numerical hybrid local and global mode-seeking tracker is validated on challenging airborne videos with heavy occlusion and large camera motions. Our approach outperforms state-of-the-art trackers on the VIVID benchmark datasets. 1

Occlusion and Motion Reasoning for Long-Term Tracking

International audienceObject tracking is a reoccurring problem in computer vision. Tracking-by-detection approaches, in particular Struck (Hare et al., 2011), have shown to be competitive in recent evaluations. However, such approaches fail in the presence of long-term occlusions as well as severe viewpoint changes of the object. In this paper we propose a principled way to combine occlusion and motion reasoning with a tracking-by-detection approach. Occlusion and motion reasoning is based on state-of-the-art long-term trajectories which are labeled as object or background tracks with an energy-based formulation. The overlap between labeled tracks and detected regions allows to identify occlusions. The motion changes of the object between consecutive frames can be estimated robustly from the geometric relation between object trajectories. If this geometric change is significant, an additional detector is trained. Experimental results show that our tracker obtains state-of-the-art results and handles occlusion and viewpoints changes better than competing tracking methods

A Stochastic Resampling Based Selective Particle Filter for Robust Visual Object Tracking

In this work, a new variant of particle filter has been proposed. In visual object tracking, particle filters have been used popularly because they are compatible with system non-linearity and non-Gaussian posterior distribution. But the main problem in particle filtering is sample degeneracy. To solve this problem, a new variant of particle filter has been proposed. The resampling algorithm used in this proposed particle filter is derived by combining systematic resampling, which is commonly used in SIR-PF (Sampling Importance Resampling Particle Filter) and a modified bat algorithm; this resampling algorithm reduces sample degeneracy as well as sample impoverishments. The measurement model is modified to handle clutter in presence of varying background. A new motion dynamics model is proposed which further reduces the chance of sample degeneracy among the particles by adaptively shifting mean of the process noise. To deal with illumination fluctuation and object deformation in presence of complete occlusion, a template update algorithm has also been proposed. This template update algorithm can update template even when the difference in the spread of the color-histogram is especially large over time. The proposed tracker has been tested against many challenging conditions and found to be robust against clutter, illumination change, scale change, fast object movement, motion blur, and complete occlusion; it has been found that the proposed algorithm outperforms the SIR-PF (Sampling Importance Resampling Particle Filter), bat algorithm and some other state-of-the-art tracking algorithms

Self-* properties of multi sensing entities in smart environments

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2005.Includes bibliographical references (p. 78-87).Computers and sensors are more and more often embedded into everyday objects, woven into garments, "painted" on architecture or deployed directly into the environment. They monitor the environment, process the information and extract knowledge that their designed and programmers hope will be interesting. As the number and variety of these sensors and their connections increase, so does the complexity of the networks in which they operate. Deployment, management, and repair become difficult to perform manually. It is, then, particularly appealing to design a software architecture that can achieve the necessary organizational structures without requiring human intervention. Focusing on image sensing and machine vision techniques, we propose to investigate how small, unspecialized, low-processing sensing entities can self-organize to create a scalable, fault tolerant, decentralized, and easily reconfigurable system for smart environments and how these entities self-adapt to optimize their contribution in the presence of constraints inherent to sensor networks.by Arnaud Pilpré.S.M