@article {12476,
	title = {Online Empirical Evaluation of Tracking Algorithms},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {32},
	year = {2010},
	month = {2010/08//},
	pages = {1443 - 1458},
	abstract = {Evaluation of tracking algorithms in the absence of ground truth is a challenging problem. There exist a variety of approaches for this problem, ranging from formal model validation techniques to heuristics that look for mismatches between track properties and the observed data. However, few of these methods scale up to the task of visual tracking, where the models are usually nonlinear and complex and typically lie in a high-dimensional space. Further, scenarios that cause track failures and/or poor tracking performance are also quite diverse for the visual tracking problem. In this paper, we propose an online performance evaluation strategy for tracking systems based on particle filters using a time-reversed Markov chain. The key intuition of our proposed methodology relies on the time-reversible nature of physical motion exhibited by most objects, which in turn should be possessed by a good tracker. In the presence of tracking failures due to occlusion, low SNR, or modeling errors, this reversible nature of the tracker is violated. We use this property for detection of track failures. To evaluate the performance of the tracker at time instant t, we use the posterior of the tracking algorithm to initialize a time-reversed Markov chain. We compute the posterior density of track parameters at the starting time t = 0 by filtering back in time to the initial time instant. The distance between the posterior density of the time-reversed chain (at t = 0) and the prior density used to initialize the tracking algorithm forms the decision statistic for evaluation. It is observed that when the data are generated by the underlying models, the decision statistic takes a low value. We provide a thorough experimental analysis of the evaluation methodology. Specifically, we demonstrate the effectiveness of our approach for tackling common challenges such as occlusion, pose, and illumination changes and provide the Receiver Operating Characteristic (ROC) curves. Finally, we also s how the applicability of the core ideas of the paper to other tracking algorithms such as the Kanade-Lucas-Tomasi (KLT) feature tracker and the mean-shift tracker.},
	keywords = {Back, Biomedical imaging, Computer vision, Filtering, formal model validation techniques, formal verification, ground truth, Kanade Lucas Tomasi feature tracker, Karhunen-Loeve transforms, lighting, Markov processes, mean shift tracker, model validation., online empirical evaluation, particle filtering (numerical methods), Particle filters, Particle tracking, performance evaluation, receiver operating characteristic curves, Robustness, SNR, Statistics, Surveillance, time reversed Markov chain, tracking, tracking algorithms, visual tracking},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2009.135},
	author = {Wu,Hao and Sankaranarayanan,A. C and Chellapa, Rama}
}
@article {13109,
	title = {Probabilistic fusion-based parameter estimation for visual tracking},
	journal = {Computer Vision and Image Understanding},
	volume = {113},
	year = {2009},
	month = {2009/04//},
	pages = {435 - 445},
	abstract = {In object tracking, visual features may not be discriminative enough to estimate high dimensional motion parameters accurately, and complex motion estimation is computationally expensive due to a large search space. To tackle these problems, a reasonable strategy is to track small components within the target independently in lower dimensional motion parameter spaces (e.g., translation only) and then estimate the overall high dimensional motion (e.g., translation, scale and rotation) by statistically integrating the individual tracking results. Although tracking each component in a lower dimensional space is more reliable and faster, it is not trivial to combine the local motion information and estimate global parameters in a robust way because the individual component motions are frequently inconsistent. We propose a robust fusion algorithm to estimate the complex motion parameters using variable-bandwidth mean-shift. By employing correlation-based uncertainty modeling and fusion of individual components, the motion parameter that is robust to outliers can be detected with variable-bandwidth density-based fusion (VBDF) algorithm. In addition, we describe a method to update target appearance model for each component adaptively based on the component motion consistency. We present various tracking results and compare the performance of our algorithm with others using real video sequences.},
	keywords = {Component-based tracking, Density-based fusion, Mean-shift, visual tracking},
	isbn = {1077-3142},
	doi = {10.1016/j.cviu.2008.11.003},
	url = {http://www.sciencedirect.com/science/article/pii/S1077314208001872},
	author = {Han,Bohyung and Davis, Larry S.}
}
@article {12682,
	title = {Visual tracking and recognition using appearance-adaptive models in particle filters},
	journal = {IEEE Transactions on Image Processing},
	volume = {13},
	year = {2004},
	month = {2004/11//},
	pages = {1491 - 1506},
	abstract = {We present an approach that incorporates appearance-adaptive models in a particle filter to realize robust visual tracking and recognition algorithms. Tracking needs modeling interframe motion and appearance changes, whereas recognition needs modeling appearance changes between frames and gallery images. In conventional tracking algorithms, the appearance model is either fixed or rapidly changing, and the motion model is simply a random walk with fixed noise variance. Also, the number of particles is typically fixed. All these factors make the visual tracker unstable. To stabilize the tracker, we propose the following modifications: an observation model arising from an adaptive appearance model, an adaptive velocity motion model with adaptive noise variance, and an adaptive number of particles. The adaptive-velocity model is derived using a first-order linear predictor based on the appearance difference between the incoming observation and the previous particle configuration. Occlusion analysis is implemented using robust statistics. Experimental results on tracking visual objects in long outdoor and indoor video sequences demonstrate the effectiveness and robustness of our tracking algorithm. We then perform simultaneous tracking and recognition by embedding them in a particle filter. For recognition purposes, we model the appearance changes between frames and gallery images by constructing the intra- and extrapersonal spaces. Accurate recognition is achieved when confronted by pose and view variations.},
	keywords = {adaptive filters, adaptive noise variance, algorithms, appearance-adaptive model, Artificial intelligence, Cluster Analysis, Computer Graphics, Computer simulation, Feedback, Filtering, first-order linear predictor, hidden feature removal, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, image recognition, Information Storage and Retrieval, Kinematics, Laboratories, Male, Models, Biological, Models, Statistical, MOTION, Movement, Noise robustness, Numerical Analysis, Computer-Assisted, occlusion analysis, Particle filters, Particle tracking, Pattern Recognition, Automated, Predictive models, Reproducibility of results, robust statistics, Sensitivity and Specificity, Signal Processing, Computer-Assisted, State estimation, statistical analysis, Subtraction Technique, tracking, Training data, visual recognition, visual tracking},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2004.836152},
	author = {Zhou,Shaohua Kevin and Chellapa, Rama and Moghaddam, B.}
}
