Abstract In this paper, we propose a novel approach for exploiting structural relations to track multiple objects that may undergo long-term occlusion and abrupt motion. We use a model-free approach that relies only on annotations given in the first frame of the video to track all the objects online, i.e. without knowledge from future frames. We initialize a probabilistic Attributed Relational Graph (ARG) from the first frame, which is incrementally updated along the video. Instead of using the structural information only to evaluate the scene, the proposed approach considers it to generate new tracking hypotheses. In this way, our method is capable of generating relevant object candidates that are used to improve or recover the track of lost objects. The proposed method is evaluated on several videos of table tennis, volleyball, and on the ACASVA dataset. The results show that our approach is very robust, flexible and able to outperform other state-of-the-art methods in sports videos that present structural patterns.
Another important issue that must be dealt with during tracking is abrupt motion. Perhaps, the simplest way to deal with it is by generating some additional target location hypotheses around the previous location to try to cover a broader region, as explored in @cite_31 . Another proposal is to solve the same problem using spatial position information for finding better candidates @cite_3 @cite_21 . This is done by dividing the image into several smaller regions and using the information obtained from the density of states of each one to find new locations. More recently, Su al @cite_6 propose relying on visual saliency information to guide the tracking and restore the target. It is important to note that, although tracking-by-detection methods should be able to deal with abrupt motion, most of them do not behave well in this situation because their association function usually enforces the temporal stability constraint. As previously mentioned, we use a different approach that relies on the structural relations between the objects to find the most likely new locations of a lost target.
@cite_31 @cite_21 @cite_6 @cite_3
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Abrupt motion is a significant challenge that commonly causes traditional tracking methods to fail. This paper presents an improved visual saliency model and integrates it to a particle filter tracker to solve this problem. Once the target is lost, our algorithm recovers tracking by detecting the target region from salient regions, which are obtained in the saliency map of current frame. In addition, to strengthen the saliency of target region, the target model is used as a prior knowledge to calculate a weight set which is utilized to construct our improved saliency map adaptively. Furthermore, we adopt the covariance descriptor as the appearance model to describe the object more accurately. Compared with several other tracking algorithms, the experimental results demonstrate that our method is more robust in dealing with various types of abrupt motion scenarios. In this paper, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise, and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target in a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an l1-regularized least-squares problem. Then, the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework. Two strategies are used to further improve the tracking performance. First, target templates are dynamically updated to capture appearance changes. Second, nonnegativity constraints are enforced to filter out clutter which negatively resembles tracking targets. We test the proposed approach on numerous sequences involving different types of challenges, including occlusion and variations in illumination, scale, and pose. The proposed approach demonstrates excellent performance in comparison with previously proposed trackers. We also extend the method for simultaneous tracking and recognition by introducing a static template set which stores target images from different classes. The recognition result at each frame is propagated to produce the final result for the whole video. The approach is validated on a vehicle tracking and classification task using outdoor infrared video sequences. Most tracking-by-detection methods employ a local search window around the predicted object location in the current frame assuming the previous location is accurate, the trajectory is smooth, and the computational capacity permits a search radius that can accommodate the maximum speed yet small enough to reduce mismatches. These, however, may not be valid always, in particular for fast and irregularly moving objects. Here, we present an object tracker that is not limited to a local search window and has ability to probe efficiently the entire frame. Our method generates a small number of "high-quality" proposals by a novel instance-specific objectness measure and evaluates them against the object model that can be adopted from an existing tracking-by-detection approach as a core tracker. During the tracking process, we update the object model concentrating on hard false-positives supplied by the proposals, which help suppressing distractors caused by difficult background clutters, and learn how to re-rank proposals according to the object model. Since we reduce significantly the number of hypotheses the core tracker evaluates, we can use richer object descriptors and stronger detector. Our method outperforms most recent state-of-the-art trackers on popular tracking benchmarks, and provides improved robustness for fast moving objects as well as for ultra low-frame-rate videos. In this paper we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, corruption and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an l 1 -regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Two additional components further improve the robustness of our approach: 1) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 2) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on five challenging sequences involving heavy occlusions, drastic illumination changes, and large pose variations. The proposed approach shows excellent performance in comparison with three previously proposed trackers. In this paper we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, corruption and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target at a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an � 1-regularized least squares problem. Then the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework in which a particle filter is used for propagating sample distributions over time. Two additional components further improve the robustness of our approach: 1) the nonnegativity constraints that help filter out clutter that is similar to tracked targets in reversed intensity patterns, and 2) a dynamic template update scheme that keeps track of the most representative templates throughout the tracking procedure. We test the proposed approach on five challenging sequences involving heavy occlusions, drastic illumination changes, and large pose variations. The proposed approach shows excellent performance in comparison with three previously proposed trackers. We propose a novel tracking algorithm based on the Wang-Landau Monte Carlo sampling method which efficiently deals with the abrupt motions. Abrupt motions could cause conventional tracking methods to fail since they violate the motion smoothness constraint. To address this problem, we introduce the Wang-Landau algorithm that has been recently proposed in statistical physics, and integrate this algorithm into the Markov Chain Monte Carlo based tracking method. Our tracking method alleviates the motion smoothness constraint utilizing both the likelihood term and the density of states term, which is estimated by the Wang-Landau algorithm. The likelihood term helps to improve the accuracy in tracking smooth motions, while the density of states term captures abrupt motions robustly. Experimental results reveal that our approach efficiently samples the object's states even in a whole state space without loss of time. Therefore, it tracks the object of which motion is drastically changing, accurately and robustly. In this paper we propose a novel Semantic Bundle Adjustment framework whereby known rigid stationary objects are detected while tracking the camera and mapping the environment. The system builds on established tracking and mapping techniques to exploit incremental 3D reconstruction in order to validate hypotheses on the presence and pose of sought objects. Then, detected objects are explicitly taken into account for a global semantic optimization of both camera and object poses. Thus, unlike all systems proposed so far, our approach allows for solving jointly the detection and SLAM problems, so as to achieve object detection together with improved SLAM accuracy. Visual tracking in unconstrained environments is very challenging due to the existence of several sources of varieties such as changes in appearance, varying lighting conditions, cluttered background, and frame-cuts. A major factor causing tracking failure is the emergence of regions having similar appearance as the target. It is even more challenging when the target leaves the field of view (FoV) leading the tracker to follow another similar object, and not reacquire the right target when it reappears. This paper presents a method to address this problem by exploiting the context on-the-fly in two terms: Distracters and Supporters. Both of them are automatically explored using a sequential randomized forest, an online template-based appearance model, and local features. Distracters are regions which have similar appearance as the target and consistently co-occur with high confidence score. The tracker must keep tracking these distracters to avoid drifting. Supporters, on the other hand, are local key-points around the target with consistent co-occurrence and motion correlation in a short time span. They play an important role in verifying the genuine target. Extensive experiments on challenging real-world video sequences show the tracking improvement when using this context information. Comparisons with several state-of-the-art approaches are also provided. We present a novel approach for multi-object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. Our approach is formulated in a minimum description length hypothesis selection framework, which allows our system to recover from mismatches and temporarily lost tracks. Building upon a state-of-the-art object detector, it performs multiview multicategory object recognition to detect cars and pedestrians in the input images. The 2D object detections are checked for their consistency with (automatically estimated) scene geometry and are converted to 3D observations which are accumulated in a world coordinate frame. A subsequent trajectory estimation module analyzes the resulting 3D observations to find physically plausible spacetime trajectories. Tracking is achieved by performing model selection after every frame. At each time instant, our approach searches for the globally optimal set of spacetime trajectories which provides the best explanation for the current image and for all evidence collected so far while satisfying the constraints that no two objects may occupy the same physical space nor explain the same image pixels at any point in time. Successful trajectory hypotheses are then fed back to guide object detection in future frames. The optimization procedure is kept efficient through incremental computation and conservative hypothesis pruning. We evaluate our approach on several challenging video sequences and demonstrate its performance on both a surveillance-type scenario and a scenario where the input videos are taken from inside a moving vehicle passing through crowded city areas. 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 show 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. This paper considers the problem of sustained multicamera tracking in the presence of occlusion and changes in the target motion model. The key insight of the proposed method is the fact that, under mild conditions, the 2D trajectories of the target in the image planes of each of the cameras are constrained to evolve in the same subspace. This observation allows for identifying, at each time instant, a single (piecewise) linear model that explains all the available 2D measurements. In turn, this model can be used in the context of a modified particle filter to predict future target locations. In the case where the target is occluded to some of the cameras, the missing measurements can be estimated using the facts that they must lie both in the subspace spanned by previous measurements and satisfy epipolar constraints. Hence, by exploiting both dynamical and geometrical constraints the proposed method can robustly handle substantial occlusion, without the need for performing 3D reconstruction, calibrated cameras or constraints on sensor separation. The performance of the proposed tracker is illustrated with several challenging examples involving targets that substantially change appearance and motion models while occluded to some of the cameras. We present a novel approach for multi-object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. It is formulated in a hypothesis selection framework and builds upon a state-of-the-art pedestrian detector. At each time instant, it searches for the globally optimal set of spacetime trajectories which provides the best explanation for the current image and for all evidence collected so far, while satisfying the constraints that no two objects may occupy the same physical space, nor explain the same image pixels at any point in time. Successful trajectory hypotheses are fed back to guide object detection in future frames. The optimization procedure is kept efficient through incremental computation and conservative hypothesis pruning. The resulting approach can initialize automatically and track a large and varying number of persons over long periods and through complex scenes with clutter, occlusions, and large-scale background changes. Also, the global optimization framework allows our system to recover from mismatches and temporarily lost tracks. We demonstrate the feasibility of the proposed approach on several challenging video sequences. Robust tracking of abrupt motion is a challenging task in computer vision due to the large motion uncertainty. In this paper, we propose a stochastic approximation Monte Carlo (SAMC) based tracking scheme for abrupt motion problem in Bayesian filtering framework. In our tracking scheme, the particle weight is dynamically estimated by learning the density of states in simulations, and thus the local-trap problem suffered by the conventional MCMC sampling-based methods could be essentially avoided. In addition, we design an adaptive SAMC sampling method to further speed up the sampling process for tracking of abrupt motion. It combines the SAMC sampling and a density grid based statistical predictive model, to give a data-mining mode embedded global sampling scheme. It is computationally efficient and effective in dealing with abrupt motion difficulties. We compare it with alternative tracking methods. Extensive experimental results showed the effectiveness and efficiency of the proposed algorithm in dealing with various types of abrupt motions. We propose a framework for general multiple target tracking, where the input is a set of candidate regions in each frame, as obtained from a state of the art background learning, and the goal is to recover trajectories of targets over time from noisy observations. Due to occlusions by targets and static objects, noisy segmentation and false alarms, one foreground region may not correspond to one target faithfully. Therefore the one-to-one assumption used in most data association algorithm is not always satisfied. Our method overcomes the one-to-one assumption by formulating the visual tracking problem in terms of finding the best spatial and temporal association of observations, which maximizes the consistency of both motion and appearance of trajectories. To avoid enumerating all possible solutions, we take a data driven Markov chain Monte Carlo (DD-MCMC) approach to sample the solution space efficiently. The sampling is driven by an informed proposal scheme controlled by a joint probability model combining motion and appearance. To make sure the Markov chain to converge to a desired distribution, we propose an automatic approach to determine the parameters in the target distribution. Comparative experiments with quantitative evaluations are provided. Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this paper, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foregrounds modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes. Sparse representation has been applied to visual tracking by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based trackers only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the target and the background, and hence may fail with more possibility when there is similar object or occlusion in the scene. In this paper we develop a simple yet robust tracking method based on the structural local sparse appearance model. This representation exploits both partial information and spatial information of the target based on a novel alignment-pooling method. The similarity obtained by pooling across the local patches helps not only locate the target more accurately but also handle occlusion. In addition, we employ a template update strategy which combines incremental subspace learning and sparse representation. This strategy adapts the template to the appearance change of the target with less possibility of drifting and reduces the influence of the occluded target template as well. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods. This paper presents a novel framework for tracking thousands of vehicles in high resolution, low frame rate, multiple camera aerial videos. The proposed algorithm avoids the pitfalls of global minimization of data association costs and instead maintains multiple object-centric associations for each track. Representation of object state in terms of many to many data associations per track is proposed and multiple novel constraints are introduced to make the association problem tractable while allowing sharing of detections among tracks. Weighted hypothetical measurements are introduced to better handle occlusions, mis-detections and split or merged detections. A two-frame differencing method is presented which performs simultaneous moving object detection in both. Two novel contextual constraints of vehicle following model, and discouragement of track intersection and merging are also proposed. Extensive experiments on challenging, ground truthed data sets are performed to show the feasibility and superiority of the proposed approach. Results of quantitative comparison with existing approaches are presented, and the efficacy of newly introduced constraints is experimentally established. The proposed algorithm performs better and faster than global, 1---1 data association methods. Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100 precision with recall rates of up to 60 . To track targets across networked cameras with disjoint views, one of the major problems is to learn the spatio-temporal relationship and the appearance relationship, where the appearance relationship is usually modeled as a brightness transfer function. Traditional methods learning the relationships by using either hand-labeled correspondence or batch-learning procedure are applicable when the environment remains unchanged. However, in many situations such as lighting changes, the environment varies seriously and hence traditional methods fail to work. In this paper, we propose an unsupervised method which learns adaptively and can be applied to long-term monitoring. Furthermore, we propose a method that can avoid weak links and discover the true valid links among the entry exit zones of cameras from the correspondence. Experimental results demonstrate that our method outperforms existing methods in learning both the spatio-temporal and the appearance relationship, and can achieve high tracking accuracy in both indoor and outdoor environment. In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking. Our proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and to recover missed detections. Furthermore, target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e., during the tracking process. Our approach is effective even when such cues fail to identify or follow the target due to occlusions or object-to-object interactions. We also propose to learn the weights of these two tracking cues to handle the difficult situations, such as severe occlusions and object-to-object interactions effectively. Our method has been validated on several public datasets and the experimental results show that it outperforms several state-of-the-art tracking methods.