@conference {12582,
	title = {In Situ Evaluation of Tracking Algorithms Using Time Reversed Chains},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {Automatic evaluation of visual tracking algorithms in the absence of ground truth is a very challenging and important problem. In the context of online appearance modeling, there is an additional ambiguity involving the correctness of the appearance model. In this paper, we propose a novel performance evaluation strategy for tracking systems based on particle filter using a time reversed Markov chain. Starting from the latest observation, the time reversed chain is propagated back till the starting time t = 0 of the tracking algorithm. The posterior density of the time reversed chain is also computed. 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 postulated that when the data is generated true to the underlying models, the decision statistic takes a low value. We empirically demonstrate the performance of the algorithm against various common failure modes in the generic visual tracking problem. Finally, we derive a small frame approximation that allows for very efficient computation of the decision statistic.},
	keywords = {(numerical, algorithm;Markov, chain;tracking, decision, density;time, detection;particle, Evaluation, evaluation;object, filter;performance, Filtering, Markov, methods);tracking;visual, processes;decision, reversed, servoing;, situ, statistics;in, strategy;posterior, systems;visual, theory;object, tracking, tracking;particle},
	doi = {10.1109/CVPR.2007.382992},
	author = {Wu,Hao and Sankaranarayanan,A. C and Chellapa, Rama}
}
@conference {13140,
	title = {Probabilistic Fusion Tracking Using Mixture Kernel-Based Bayesian Filtering},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {Even though sensor fusion techniques based on particle filters have been applied to object tracking, their implementations have been limited to combining measurements from multiple sensors by the simple product of individual likelihoods. Therefore, the number of observations is increased as many times as the number of sensors, and the combined observation may become unreliable through blind integration of sensor observations - especially if some sensors are too noisy and non-discriminative. We describe a methodology to model interactions between multiple sensors and to estimate the current state by using a mixture of Bayesian filters - one filter for each sensor, where each filter makes a different level of contribution to estimate the combined posterior in a reliable manner. In this framework, an adaptive particle arrangement system is constructed in which each particle is allocated to only one of the sensors for observation and a different number of samples is assigned to each sensor using prior distribution and partial observations. We apply this technique to visual tracking in logical and physical sensor fusion frameworks, and demonstrate its effectiveness through tracking results.},
	keywords = {(numerical, adaptive, arrangement, Bayesian, Filtering, filtering;multiple, filters;probabilistic, fusion, fusion;tracking;, integration;mixture, kernel-based, methods);sensor, methods;array, particle, processing;particle, sensors;object, signal, system;blind, techniques;visual, tracking;Bayes, tracking;particle, tracking;sensor},
	doi = {10.1109/ICCV.2007.4408938},
	author = {Han,Bohyung and Joo,Seong-Wook and Davis, Larry S.}
}
@article {12587,
	title = {Target Tracking Using a Joint Acoustic Video System},
	journal = {Multimedia, IEEE Transactions on},
	volume = {9},
	year = {2007},
	month = {2007/06//},
	pages = {715 - 727},
	abstract = {In this paper, a multitarget tracking system for collocated video and acoustic sensors is presented. We formulate the tracking problem using a particle filter based on a state-space approach. We first discuss the acoustic state-space formulation whose observations use a sliding window of direction-of-arrival estimates. We then present the video state space that tracks a target{\textquoteright}s position on the image plane based on online adaptive appearance models. For the joint operation of the filter, we combine the state vectors of the individual modalities and also introduce a time-delay variable to handle the acoustic-video data synchronization issue, caused by acoustic propagation delays. A novel particle filter proposal strategy for joint state-space tracking is introduced, which places the random support of the joint filter where the final posterior is likely to lie. By using the Kullback-Leibler divergence measure, it is shown that the joint operation of the filter decreases the worst case divergence of the individual modalities. The resulting joint tracking filter is quite robust against video and acoustic occlusions due to our proposal strategy. Computer simulations are presented with synthetic and field data to demonstrate the filter{\textquoteright}s performance},
	keywords = {(numerical, acoustic, adaptive, appearance, approach;synchronization;time-delay, data, delay;acoustic, divergence;acoustic, estimate;joint, estimation;hidden, feature, filter;sliding, Filtering, fusion;multitarget, fusion;synchronisation;target, highways;direction-of-arrival, Kullback-Leibler, methods);sensor, model;particle, processing;, processing;automated, propagation, removal;optical, signal, system;multimodal, tracking;acoustic, tracking;direction-of-arrival, tracking;occlusion;online, tracking;particle, tracking;video, variable;visual, video, window;state-space},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2007.893340},
	author = {Cevher, V. and Sankaranarayanan,A. C and McClellan, J.H. and Chellapa, Rama}
}
