Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling

Stochastic sampling based trackers have shown good performance for abrupt motion tracking so that they have gained popularity in recent years. However, conventional methods tend to use a two-stage sampling paradigm, in which the search space needs to be uniformly explored with an inefficient preliminary sampling phase. In this paper, we propose a novel sampling-based method in the Bayesian filtering framework to address the problem. Within the framework, nearest neighbor field estimation is utilized to compute the importance proposal probabilities, which guide the Markov chain search towards promising regions and thus enhance the sampling efficiency; given the motion priors, a smoothing stochastic sampling Monte Carlo algorithm is proposed to approximate the posterior distribution through a smoothing weight-updating scheme. Moreover, to track the abrupt and the smooth motions simultaneously, we develop an abrupt-motion detection scheme which can discover the presence of abrupt motions during online tracking. Extensive experiments on challenging image sequences demonstrate the effectiveness and the robustness of our algorithm in handling the abrupt motions.Comment: submitted to Elsevier Neurocomputin

Estimating the granularity coefficient of a Potts-Markov random field within an MCMC algorithm

This paper addresses the problem of estimating the Potts parameter B jointly with the unknown parameters of a Bayesian model within a Markov chain Monte Carlo (MCMC) algorithm. Standard MCMC methods cannot be applied to this problem because performing inference on B requires computing the intractable normalizing constant of the Potts model. In the proposed MCMC method the estimation of B is conducted using a likelihood-free Metropolis-Hastings algorithm. Experimental results obtained for synthetic data show that estimating B jointly with the other unknown parameters leads to estimation results that are as good as those obtained with the actual value of B. On the other hand, assuming that the value of B is known can degrade estimation performance significantly if this value is incorrect. To illustrate the interest of this method, the proposed algorithm is successfully applied to real bidimensional SAR and tridimensional ultrasound images

Activity Analysis; Finding Explanations for Sets of Events

Automatic activity recognition is the computational process of analysing visual input and reasoning about detections to understand the performed events. In all but the simplest scenarios, an activity involves multiple interleaved events, some related and others independent. The activity in a car park or at a playground would typically include many events. This research assumes the possible events and any constraints between the events can be defined for the given scene. Analysing the activity should thus recognise a complete and consistent set of events; this is referred to as a global explanation of the activity. By seeking a global explanation that satisfies the activity’s constraints, infeasible interpretations can be avoided, and ambiguous observations may be resolved. An activity’s events and any natural constraints are defined using a grammar formalism. Attribute Multiset Grammars (AMG) are chosen because they allow defining hierarchies, as well as attribute rules and constraints. When used for recognition, detectors are employed to gather a set of detections. Parsing the set of detections by the AMG provides a global explanation. To find the best parse tree given a set of detections, a Bayesian network models the probability distribution over the space of possible parse trees. Heuristic and exhaustive search techniques are proposed to find the maximum a posteriori global explanation. The framework is tested for two activities: the activity in a bicycle rack, and around a building entrance. The first case study involves people locking bicycles onto a bicycle rack and picking them up later. The best global explanation for all detections gathered during the day resolves local ambiguities from occlusion or clutter. Intensive testing on 5 full days proved global analysis achieves higher recognition rates. The second case study tracks people and any objects they are carrying as they enter and exit a building entrance. A complete sequence of the person entering and exiting multiple times is recovered by the global explanation

Motion likelihood and proposal modeling in Model-Based Stochastic Tracking

Particle filtering is now established as one of the most popular methods for visual tracking. Within this framework, there are two important considerations. The first one refers to the generic assumption that the observations are temporally independent given the sequence of object states. The second consideration, often made in the literature, uses the transition prior as proposal distribution. Thus, the current observations are not taken into account, requesting the noise process of this prior to be large enough to handle abrupt trajectory changes. As a result, many particles are either wasted in low likelihood regions of the state space, resulting in low sampling efficiency, or more importantly, propagated to distractor regions of the image, resulting in tracking failures. In this paper, we propose to handle both considerations using motion. We first argue that in general observations are conditionally correlated, and propose a new model to account for this correlation allowing for the natural introduction of implicit and/or explicit motion measurements in the likelihood term. Secondly, explicit motion measurements are used to drive the sampling process towards the most likely regions of the state space. Overall, the proposed model allows to handle abrupt motion changes and to filter out visual distractors when tracking objects with generic models based on shape or color distribution. Experimental results obtained on head tracking, using several sequences with moving camera involving large dynamics, and compared against the CONDENSATION algorithm, have demonstrated superior tracking performance of our approach

Improved nonlinear filtering for target tracking.

The objective of this research is to develop robust and accurate tracking algorithms for various tracking applications. These tracking problems can be formulated as nonlinear filtering problems. The tracking algorithms will be developed based on an emerging promising nonlinear filter technique, known as sequential importance sampling (nick-name: particle filtering). This technique was introduced to the engineering community in the early years of 2000, and it has recently drawn significant attention from engineers and researchers in a wide range of areas. Despite the encouraging results reported in the current literature, there are still many open questions to be answered. For the first time, the major research effort will be focusing on making improvement to the particle filter based tracking algorithm in the following three aspects: (I) refining the particle filtering process by designing better proposal distributions (II) refining the dynamic model by using multiple-model method, (i.e. using switching dynamics and jump Markov process) and (III) refining system measurements by incorporating a data fusion stage for multiple measurement cues

Estimation And Tracking Algorithm For Autonomous Vehicles And Humans

Autonomous driving systems have experienced impressive growth in recent years. The present research community is working on several challenging aspects, such as, tracking, localization, path planning and control. In this thesis, first, we focus on tracking system and present a method to accurately track a moving vehicle. In the vehicle tracking, considering the proximity of surrounding vehicles, it is critical to detect their unusual maneuvers as quickly as possible, especially when autonomous vehicles operate among human-operated traffic. In this work, we present an approach to quickly detect lane-changing maneuvers of the nearby vehicles. The proposed algorithm is based on the optimal likelihood ratio test, known as Page test. Second, we consider another form of tracking: tracking the movements of humans in indoor settings. Indoor localization of staff and patients based on radio frequency identification (RFID) technology has promising potential application in the healthcare sector. The use of an active RFID in real-time indoor positioning system without any sacrifice of localization accuracy is intended to provide security, guidance and support service to patients. In this paper maximum likelihood estimation along with its Cramer-Rao lower bound of the locations of active RFID tags are presented by exploring the received signal strength indicator which is collected at the readers. The performance of real-time localization system is implemented by using an extended Kalman filter (EKF)

Bayesian Estimation and Quality Monitoring for Personal Positioning Systems

Personal positioning is a dynamic estimation problem where the ability to assess the quality of the positioning service is as important as obtaining accurate position estimates. When estimating the position of a person, as opposed to e.g. an airplane, the type of motion can change at any time as a pedestrian can board a bus, or a cyclist can board a train. Also the changing surroundings in urban navigation influence the observation noise as tall buildings blocking the line of sight to satellites are full of reflecting surfaces. First we investigate classic robust estimation methods applied to the positioning problem, but then we focus on the Bayesian framework, as its generality allows us to take into account the abrupt changes in the state-space system. Gaussian mixture distributions and Markov chain indicator processes are used to model the changing systems. We evaluate the resulting systems mainly with sequential Monte Carlo methods, as this approach gives us an approximative joint posterior distribution of the errors and the state. We propose a general framework for the Bayesian receiver autonomous integrity monitoring in urban navigation based on the posterior probabilities. We also use the Bayesian framework to solve the explicit effect of the sensor errors in a nominal system that estimates the state with the assumption of no changes in the models. We use the estimated cumulated effect of the errors in the time series to determine whether error is present in the system at any time. Finally, a variational Bayes algorithm is developed for detecting changes in the system noise covariances

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

Discriminative tracking using tensor pooling

How to effectively organize local descriptors to build a global representation has a critical impact on the performance of vision tasks. Recently, local sparse representation has been successfully applied to visual tracking, owing to its discriminative nature and robustness against local noise and partial occlusions. Local sparse codes computed with a template actually form a three-order tensor according to their original layout, although most existing pooling operators convert the codes to a vector by concatenating or computing statistics on them. We argue that, compared to pooling vectors, the tensor form could deliver more intrinsic structural information for the target appearance, and can also avoid high dimensionality learning problems suffered in concatenation-based pooling methods. Therefore, in this paper, we propose to represent target templates and candidates directly with sparse coding tensors, and build the appearance model by incrementally learning on these tensors. We propose a discriminative framework to further improve robustness of our method against drifting and environmental noise. Experiments on a recent comprehensive benchmark indicate that our method performs better than state-of-the-art trackers